Line_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Top 500 Indian Cities\n",
    "## Data of 500 Cities with population more than 1 Lac by Census 2011\n",
    "It is to visualize where the future cities of India stands today"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Preparing Data for visualization* <a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we'll do is import two key data analysis modules: **Pandas** and **Numpy**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us import our primary Data of 500 Cities using *pandas* read_csv() method\n",
    "Now let us read in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read into a pandas dataframe!\n"
     ]
    }
   ],
   "source": [
    "dt = pd.read_csv('cities_r2.csv')\n",
    "print ('Data read into a pandas dataframe!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the top 5 rows of the dataset using the `head()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>state_code</th>\n",
       "      <th>state_name</th>\n",
       "      <th>dist_code</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>...</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>location</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>3</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>9</td>\n",
       "      <td>145238</td>\n",
       "      <td>76840</td>\n",
       "      <td>68398</td>\n",
       "      <td>15870</td>\n",
       "      <td>8587</td>\n",
       "      <td>7283</td>\n",
       "      <td>...</td>\n",
       "      <td>44972</td>\n",
       "      <td>890</td>\n",
       "      <td>848</td>\n",
       "      <td>79.86</td>\n",
       "      <td>85.49</td>\n",
       "      <td>73.59</td>\n",
       "      <td>30.1452928,74.1993043</td>\n",
       "      <td>16287</td>\n",
       "      <td>8612</td>\n",
       "      <td>7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Achalpur</td>\n",
       "      <td>27</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>7</td>\n",
       "      <td>112293</td>\n",
       "      <td>58256</td>\n",
       "      <td>54037</td>\n",
       "      <td>11810</td>\n",
       "      <td>6186</td>\n",
       "      <td>5624</td>\n",
       "      <td>...</td>\n",
       "      <td>43086</td>\n",
       "      <td>928</td>\n",
       "      <td>909</td>\n",
       "      <td>91.99</td>\n",
       "      <td>94.77</td>\n",
       "      <td>89.00</td>\n",
       "      <td>21.257584,77.5086754</td>\n",
       "      <td>8863</td>\n",
       "      <td>5269</td>\n",
       "      <td>3594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adilabad</td>\n",
       "      <td>28</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>1</td>\n",
       "      <td>117388</td>\n",
       "      <td>59232</td>\n",
       "      <td>58156</td>\n",
       "      <td>13103</td>\n",
       "      <td>6731</td>\n",
       "      <td>6372</td>\n",
       "      <td>...</td>\n",
       "      <td>37660</td>\n",
       "      <td>982</td>\n",
       "      <td>947</td>\n",
       "      <td>80.51</td>\n",
       "      <td>88.18</td>\n",
       "      <td>72.73</td>\n",
       "      <td>19.0809075,79.560344</td>\n",
       "      <td>10565</td>\n",
       "      <td>6797</td>\n",
       "      <td>3768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adityapur</td>\n",
       "      <td>20</td>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>24</td>\n",
       "      <td>173988</td>\n",
       "      <td>91495</td>\n",
       "      <td>82493</td>\n",
       "      <td>23042</td>\n",
       "      <td>12063</td>\n",
       "      <td>10979</td>\n",
       "      <td>...</td>\n",
       "      <td>54515</td>\n",
       "      <td>902</td>\n",
       "      <td>910</td>\n",
       "      <td>83.46</td>\n",
       "      <td>89.98</td>\n",
       "      <td>76.23</td>\n",
       "      <td>22.7834741,86.1576889</td>\n",
       "      <td>19225</td>\n",
       "      <td>12189</td>\n",
       "      <td>7036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adoni</td>\n",
       "      <td>28</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>21</td>\n",
       "      <td>166537</td>\n",
       "      <td>82743</td>\n",
       "      <td>83794</td>\n",
       "      <td>18406</td>\n",
       "      <td>9355</td>\n",
       "      <td>9051</td>\n",
       "      <td>...</td>\n",
       "      <td>45089</td>\n",
       "      <td>1013</td>\n",
       "      <td>968</td>\n",
       "      <td>68.38</td>\n",
       "      <td>76.58</td>\n",
       "      <td>60.33</td>\n",
       "      <td>15.6322227,77.2728368</td>\n",
       "      <td>11902</td>\n",
       "      <td>7871</td>\n",
       "      <td>4031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  name_of_city  state_code      state_name  dist_code  population_total  \\\n",
       "0      Abohar            3          PUNJAB          9            145238   \n",
       "1    Achalpur           27     MAHARASHTRA          7            112293   \n",
       "2    Adilabad           28  ANDHRA PRADESH          1            117388   \n",
       "3   Adityapur           20       JHARKHAND         24            173988   \n",
       "4       Adoni           28  ANDHRA PRADESH         21            166537   \n",
       "\n",
       "   population_male  population_female  0-6_population_total  \\\n",
       "0            76840              68398                 15870   \n",
       "1            58256              54037                 11810   \n",
       "2            59232              58156                 13103   \n",
       "3            91495              82493                 23042   \n",
       "4            82743              83794                 18406   \n",
       "\n",
       "   0-6_population_male  0-6_population_female  ...  literates_female  \\\n",
       "0                 8587                   7283  ...             44972   \n",
       "1                 6186                   5624  ...             43086   \n",
       "2                 6731                   6372  ...             37660   \n",
       "3                12063                  10979  ...             54515   \n",
       "4                 9355                   9051  ...             45089   \n",
       "\n",
       "   sex_ratio  child_sex_ratio  effective_literacy_rate_total  \\\n",
       "0        890              848                          79.86   \n",
       "1        928              909                          91.99   \n",
       "2        982              947                          80.51   \n",
       "3        902              910                          83.46   \n",
       "4       1013              968                          68.38   \n",
       "\n",
       "   effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "0                         85.49                           73.59   \n",
       "1                         94.77                           89.00   \n",
       "2                         88.18                           72.73   \n",
       "3                         89.98                           76.23   \n",
       "4                         76.58                           60.33   \n",
       "\n",
       "                location  total_graduates male_graduates  female_graduates  \n",
       "0  30.1452928,74.1993043            16287           8612              7675  \n",
       "1   21.257584,77.5086754             8863           5269              3594  \n",
       "2   19.0809075,79.560344            10565           6797              3768  \n",
       "3  22.7834741,86.1576889            19225          12189              7036  \n",
       "4  15.6322227,77.2728368            11902           7871              4031  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()\n",
    "# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also veiw the bottom 5 rows of the dataset using the `tail()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>state_code</th>\n",
       "      <th>state_name</th>\n",
       "      <th>dist_code</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>...</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>location</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Vizianagaram</td>\n",
       "      <td>28</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>12</td>\n",
       "      <td>227533</td>\n",
       "      <td>111596</td>\n",
       "      <td>115937</td>\n",
       "      <td>20487</td>\n",
       "      <td>10495</td>\n",
       "      <td>9992</td>\n",
       "      <td>...</td>\n",
       "      <td>80306</td>\n",
       "      <td>1039</td>\n",
       "      <td>952</td>\n",
       "      <td>81.85</td>\n",
       "      <td>88.18</td>\n",
       "      <td>75.80</td>\n",
       "      <td>18.1066576,83.3955506</td>\n",
       "      <td>30656</td>\n",
       "      <td>19173</td>\n",
       "      <td>11483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Warangal</td>\n",
       "      <td>28</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>9</td>\n",
       "      <td>620116</td>\n",
       "      <td>310400</td>\n",
       "      <td>309716</td>\n",
       "      <td>55392</td>\n",
       "      <td>28434</td>\n",
       "      <td>26958</td>\n",
       "      <td>...</td>\n",
       "      <td>217136</td>\n",
       "      <td>998</td>\n",
       "      <td>948</td>\n",
       "      <td>84.16</td>\n",
       "      <td>91.54</td>\n",
       "      <td>76.79</td>\n",
       "      <td>17.9689008,79.5940544</td>\n",
       "      <td>109613</td>\n",
       "      <td>69507</td>\n",
       "      <td>40106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Wardha</td>\n",
       "      <td>27</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>8</td>\n",
       "      <td>105543</td>\n",
       "      <td>53241</td>\n",
       "      <td>52302</td>\n",
       "      <td>9754</td>\n",
       "      <td>5139</td>\n",
       "      <td>4615</td>\n",
       "      <td>...</td>\n",
       "      <td>43339</td>\n",
       "      <td>982</td>\n",
       "      <td>898</td>\n",
       "      <td>94.05</td>\n",
       "      <td>97.19</td>\n",
       "      <td>90.88</td>\n",
       "      <td>20.745319,78.6021946</td>\n",
       "      <td>19363</td>\n",
       "      <td>10645</td>\n",
       "      <td>8718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Yamunanagar</td>\n",
       "      <td>6</td>\n",
       "      <td>HARYANA</td>\n",
       "      <td>3</td>\n",
       "      <td>216628</td>\n",
       "      <td>115404</td>\n",
       "      <td>101224</td>\n",
       "      <td>22905</td>\n",
       "      <td>12556</td>\n",
       "      <td>10349</td>\n",
       "      <td>...</td>\n",
       "      <td>74255</td>\n",
       "      <td>877</td>\n",
       "      <td>824</td>\n",
       "      <td>85.91</td>\n",
       "      <td>89.61</td>\n",
       "      <td>81.71</td>\n",
       "      <td>30.1290485,77.2673901</td>\n",
       "      <td>29803</td>\n",
       "      <td>14349</td>\n",
       "      <td>15454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Yavatmal</td>\n",
       "      <td>27</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>14</td>\n",
       "      <td>116714</td>\n",
       "      <td>58717</td>\n",
       "      <td>57997</td>\n",
       "      <td>11081</td>\n",
       "      <td>5894</td>\n",
       "      <td>5187</td>\n",
       "      <td>...</td>\n",
       "      <td>46561</td>\n",
       "      <td>988</td>\n",
       "      <td>880</td>\n",
       "      <td>91.81</td>\n",
       "      <td>95.45</td>\n",
       "      <td>88.17</td>\n",
       "      <td>20.3887937,78.1204073</td>\n",
       "      <td>13511</td>\n",
       "      <td>7397</td>\n",
       "      <td>6114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      name_of_city  state_code      state_name  dist_code  population_total  \\\n",
       "488  Vizianagaram           28  ANDHRA PRADESH         12            227533   \n",
       "489      Warangal           28  ANDHRA PRADESH          9            620116   \n",
       "490        Wardha           27     MAHARASHTRA          8            105543   \n",
       "491   Yamunanagar            6         HARYANA          3            216628   \n",
       "492      Yavatmal           27     MAHARASHTRA         14            116714   \n",
       "\n",
       "     population_male  population_female  0-6_population_total  \\\n",
       "488           111596             115937                 20487   \n",
       "489           310400             309716                 55392   \n",
       "490            53241              52302                  9754   \n",
       "491           115404             101224                 22905   \n",
       "492            58717              57997                 11081   \n",
       "\n",
       "     0-6_population_male  0-6_population_female  ...  literates_female  \\\n",
       "488                10495                   9992  ...             80306   \n",
       "489                28434                  26958  ...            217136   \n",
       "490                 5139                   4615  ...             43339   \n",
       "491                12556                  10349  ...             74255   \n",
       "492                 5894                   5187  ...             46561   \n",
       "\n",
       "     sex_ratio  child_sex_ratio  effective_literacy_rate_total  \\\n",
       "488       1039              952                          81.85   \n",
       "489        998              948                          84.16   \n",
       "490        982              898                          94.05   \n",
       "491        877              824                          85.91   \n",
       "492        988              880                          91.81   \n",
       "\n",
       "     effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "488                         88.18                           75.80   \n",
       "489                         91.54                           76.79   \n",
       "490                         97.19                           90.88   \n",
       "491                         89.61                           81.71   \n",
       "492                         95.45                           88.17   \n",
       "\n",
       "                  location  total_graduates male_graduates  female_graduates  \n",
       "488  18.1066576,83.3955506            30656          19173             11483  \n",
       "489  17.9689008,79.5940544           109613          69507             40106  \n",
       "490   20.745319,78.6021946            19363          10645              8718  \n",
       "491  30.1290485,77.2673901            29803          14349             15454  \n",
       "492  20.3887937,78.1204073            13511           7397              6114  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 493 entries, 0 to 492\n",
      "Data columns (total 22 columns):\n",
      "name_of_city                      493 non-null object\n",
      "state_code                        493 non-null int64\n",
      "state_name                        493 non-null object\n",
      "dist_code                         493 non-null int64\n",
      "population_total                  493 non-null int64\n",
      "population_male                   493 non-null int64\n",
      "population_female                 493 non-null int64\n",
      "0-6_population_total              493 non-null int64\n",
      "0-6_population_male               493 non-null int64\n",
      "0-6_population_female             493 non-null int64\n",
      "literates_total                   493 non-null int64\n",
      "literates_male                    493 non-null int64\n",
      "literates_female                  493 non-null int64\n",
      "sex_ratio                         493 non-null int64\n",
      "child_sex_ratio                   493 non-null int64\n",
      "effective_literacy_rate_total     493 non-null float64\n",
      "effective_literacy_rate_male      493 non-null float64\n",
      "effective_literacy_rate_female    493 non-null float64\n",
      "location                          493 non-null object\n",
      "total_graduates                   493 non-null int64\n",
      "male_graduates                    493 non-null int64\n",
      "female_graduates                  493 non-null int64\n",
      "dtypes: float64(3), int64(16), object(3)\n",
      "memory usage: 84.8+ KB\n"
     ]
    }
   ],
   "source": [
    "dt.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The default type of index and columns is NOT list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.base.Index'>\n",
      "<class 'pandas.core.indexes.range.RangeIndex'>\n"
     ]
    }
   ],
   "source": [
    "print(type(dt.columns))\n",
    "print(type(dt.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the index and columns as lists, we can use the `tolist()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "dt.columns.tolist()\n",
    "dt.index.tolist()\n",
    "\n",
    "print (type(dt.columns.tolist()))\n",
    "print (type(dt.index.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view the dimensions of the dataframe, we use the `.shape` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(493, 22)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of dataframe (rows, columns)\n",
    "dt.shape    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The main types stored in *pandas* objects are *float*, *int*, *bool*, *datetime64[ns]* and *datetime64[ns, tz] (in >= 0.17.0)*, *timedelta[ns]*, *category (in >= 0.15.0)*, and *object* (string). In addition these dtypes have item sizes, e.g. int64 and int32. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>state_name</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>145238</td>\n",
       "      <td>76840</td>\n",
       "      <td>68398</td>\n",
       "      <td>15870</td>\n",
       "      <td>8587</td>\n",
       "      <td>7283</td>\n",
       "      <td>103319</td>\n",
       "      <td>58347</td>\n",
       "      <td>44972</td>\n",
       "      <td>890</td>\n",
       "      <td>848</td>\n",
       "      <td>79.86</td>\n",
       "      <td>85.49</td>\n",
       "      <td>73.59</td>\n",
       "      <td>16287</td>\n",
       "      <td>8612</td>\n",
       "      <td>7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Achalpur</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>112293</td>\n",
       "      <td>58256</td>\n",
       "      <td>54037</td>\n",
       "      <td>11810</td>\n",
       "      <td>6186</td>\n",
       "      <td>5624</td>\n",
       "      <td>92433</td>\n",
       "      <td>49347</td>\n",
       "      <td>43086</td>\n",
       "      <td>928</td>\n",
       "      <td>909</td>\n",
       "      <td>91.99</td>\n",
       "      <td>94.77</td>\n",
       "      <td>89.00</td>\n",
       "      <td>8863</td>\n",
       "      <td>5269</td>\n",
       "      <td>3594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name_of_city   state_name  population_total  population_male  \\\n",
       "0      Abohar        PUNJAB            145238            76840   \n",
       "1    Achalpur   MAHARASHTRA            112293            58256   \n",
       "\n",
       "   population_female  0-6_population_total  0-6_population_male  \\\n",
       "0              68398                 15870                 8587   \n",
       "1              54037                 11810                 6186   \n",
       "\n",
       "   0-6_population_female  literates_total  literates_male  literates_female  \\\n",
       "0                   7283           103319           58347             44972   \n",
       "1                   5624            92433           49347             43086   \n",
       "\n",
       "   sex_ratio  child_sex_ratio  effective_literacy_rate_total  \\\n",
       "0        890              848                          79.86   \n",
       "1        928              909                          91.99   \n",
       "\n",
       "   effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "0                         85.49                           73.59   \n",
       "1                         94.77                           89.00   \n",
       "\n",
       "   total_graduates  male_graduates  female_graduates  \n",
       "0            16287            8612              7675  \n",
       "1             8863            5269              3594  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
    "dt.drop(['state_code', 'dist_code', 'location'], axis=1, inplace=True)\n",
    "dt.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check to see how many null objects we have in the dataset as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name_of_city                      0\n",
       "state_name                        0\n",
       "population_total                  0\n",
       "population_male                   0\n",
       "population_female                 0\n",
       "0-6_population_total              0\n",
       "0-6_population_male               0\n",
       "0-6_population_female             0\n",
       "literates_total                   0\n",
       "literates_male                    0\n",
       "literates_female                  0\n",
       "sex_ratio                         0\n",
       "child_sex_ratio                   0\n",
       "effective_literacy_rate_total     0\n",
       "effective_literacy_rate_male      0\n",
       "effective_literacy_rate_female    0\n",
       "total_graduates                   0\n",
       "male_graduates                    0\n",
       "female_graduates                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 293. This makes it very difficult to do a query by a specific state. For example to search for data on RAJASTHAN, we need to know the corressponding index value.\n",
    "\n",
    "This can be fixed very easily by setting the 'state_name' column as the index using `set_index()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.set_index('state_name', inplace=True)\n",
    "# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PUNJAB</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>145238</td>\n",
       "      <td>76840</td>\n",
       "      <td>68398</td>\n",
       "      <td>15870</td>\n",
       "      <td>8587</td>\n",
       "      <td>7283</td>\n",
       "      <td>103319</td>\n",
       "      <td>58347</td>\n",
       "      <td>44972</td>\n",
       "      <td>890</td>\n",
       "      <td>848</td>\n",
       "      <td>79.86</td>\n",
       "      <td>85.49</td>\n",
       "      <td>73.59</td>\n",
       "      <td>16287</td>\n",
       "      <td>8612</td>\n",
       "      <td>7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAHARASHTRA</th>\n",
       "      <td>Achalpur</td>\n",
       "      <td>112293</td>\n",
       "      <td>58256</td>\n",
       "      <td>54037</td>\n",
       "      <td>11810</td>\n",
       "      <td>6186</td>\n",
       "      <td>5624</td>\n",
       "      <td>92433</td>\n",
       "      <td>49347</td>\n",
       "      <td>43086</td>\n",
       "      <td>928</td>\n",
       "      <td>909</td>\n",
       "      <td>91.99</td>\n",
       "      <td>94.77</td>\n",
       "      <td>89.00</td>\n",
       "      <td>8863</td>\n",
       "      <td>5269</td>\n",
       "      <td>3594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDHRA PRADESH</th>\n",
       "      <td>Adilabad</td>\n",
       "      <td>117388</td>\n",
       "      <td>59232</td>\n",
       "      <td>58156</td>\n",
       "      <td>13103</td>\n",
       "      <td>6731</td>\n",
       "      <td>6372</td>\n",
       "      <td>83955</td>\n",
       "      <td>46295</td>\n",
       "      <td>37660</td>\n",
       "      <td>982</td>\n",
       "      <td>947</td>\n",
       "      <td>80.51</td>\n",
       "      <td>88.18</td>\n",
       "      <td>72.73</td>\n",
       "      <td>10565</td>\n",
       "      <td>6797</td>\n",
       "      <td>3768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JHARKHAND</th>\n",
       "      <td>Adityapur</td>\n",
       "      <td>173988</td>\n",
       "      <td>91495</td>\n",
       "      <td>82493</td>\n",
       "      <td>23042</td>\n",
       "      <td>12063</td>\n",
       "      <td>10979</td>\n",
       "      <td>125985</td>\n",
       "      <td>71470</td>\n",
       "      <td>54515</td>\n",
       "      <td>902</td>\n",
       "      <td>910</td>\n",
       "      <td>83.46</td>\n",
       "      <td>89.98</td>\n",
       "      <td>76.23</td>\n",
       "      <td>19225</td>\n",
       "      <td>12189</td>\n",
       "      <td>7036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDHRA PRADESH</th>\n",
       "      <td>Adoni</td>\n",
       "      <td>166537</td>\n",
       "      <td>82743</td>\n",
       "      <td>83794</td>\n",
       "      <td>18406</td>\n",
       "      <td>9355</td>\n",
       "      <td>9051</td>\n",
       "      <td>101292</td>\n",
       "      <td>56203</td>\n",
       "      <td>45089</td>\n",
       "      <td>1013</td>\n",
       "      <td>968</td>\n",
       "      <td>68.38</td>\n",
       "      <td>76.58</td>\n",
       "      <td>60.33</td>\n",
       "      <td>11902</td>\n",
       "      <td>7871</td>\n",
       "      <td>4031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name_of_city  population_total  population_male  \\\n",
       "state_name                                                       \n",
       "PUNJAB              Abohar             145238            76840   \n",
       "MAHARASHTRA       Achalpur             112293            58256   \n",
       "ANDHRA PRADESH    Adilabad             117388            59232   \n",
       "JHARKHAND        Adityapur             173988            91495   \n",
       "ANDHRA PRADESH       Adoni             166537            82743   \n",
       "\n",
       "                population_female  0-6_population_total  0-6_population_male  \\\n",
       "state_name                                                                     \n",
       "PUNJAB                      68398                 15870                 8587   \n",
       "MAHARASHTRA                 54037                 11810                 6186   \n",
       "ANDHRA PRADESH              58156                 13103                 6731   \n",
       "JHARKHAND                   82493                 23042                12063   \n",
       "ANDHRA PRADESH              83794                 18406                 9355   \n",
       "\n",
       "                0-6_population_female  literates_total  literates_male  \\\n",
       "state_name                                                               \n",
       "PUNJAB                           7283           103319           58347   \n",
       "MAHARASHTRA                      5624            92433           49347   \n",
       "ANDHRA PRADESH                   6372            83955           46295   \n",
       "JHARKHAND                       10979           125985           71470   \n",
       "ANDHRA PRADESH                   9051           101292           56203   \n",
       "\n",
       "                literates_female  sex_ratio  child_sex_ratio  \\\n",
       "state_name                                                     \n",
       "PUNJAB                     44972        890              848   \n",
       "MAHARASHTRA                43086        928              909   \n",
       "ANDHRA PRADESH             37660        982              947   \n",
       "JHARKHAND                  54515        902              910   \n",
       "ANDHRA PRADESH             45089       1013              968   \n",
       "\n",
       "                effective_literacy_rate_total  effective_literacy_rate_male  \\\n",
       "state_name                                                                    \n",
       "PUNJAB                                  79.86                         85.49   \n",
       "MAHARASHTRA                             91.99                         94.77   \n",
       "ANDHRA PRADESH                          80.51                         88.18   \n",
       "JHARKHAND                               83.46                         89.98   \n",
       "ANDHRA PRADESH                          68.38                         76.58   \n",
       "\n",
       "                effective_literacy_rate_female  total_graduates  \\\n",
       "state_name                                                        \n",
       "PUNJAB                                   73.59            16287   \n",
       "MAHARASHTRA                              89.00             8863   \n",
       "ANDHRA PRADESH                           72.73            10565   \n",
       "JHARKHAND                                76.23            19225   \n",
       "ANDHRA PRADESH                           60.33            11902   \n",
       "\n",
       "                male_graduates  female_graduates  \n",
       "state_name                                        \n",
       "PUNJAB                    8612              7675  \n",
       "MAHARASHTRA               5269              3594  \n",
       "ANDHRA PRADESH            6797              3768  \n",
       "JHARKHAND                12189              7036  \n",
       "ANDHRA PRADESH            7871              4031  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's view a quick summary of each column in our dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.481124e+05</td>\n",
       "      <td>2.343468e+05</td>\n",
       "      <td>2.137656e+05</td>\n",
       "      <td>4.709285e+04</td>\n",
       "      <td>24849.527383</td>\n",
       "      <td>22243.320487</td>\n",
       "      <td>3.461527e+05</td>\n",
       "      <td>1.894384e+05</td>\n",
       "      <td>1.567143e+05</td>\n",
       "      <td>930.294118</td>\n",
       "      <td>902.332657</td>\n",
       "      <td>85.131460</td>\n",
       "      <td>89.920162</td>\n",
       "      <td>79.967181</td>\n",
       "      <td>6.620236e+04</td>\n",
       "      <td>3.771556e+04</td>\n",
       "      <td>2.848680e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.033228e+06</td>\n",
       "      <td>5.487786e+05</td>\n",
       "      <td>4.848622e+05</td>\n",
       "      <td>1.050279e+05</td>\n",
       "      <td>55535.310272</td>\n",
       "      <td>49523.241379</td>\n",
       "      <td>8.220952e+05</td>\n",
       "      <td>4.534753e+05</td>\n",
       "      <td>3.690677e+05</td>\n",
       "      <td>55.849106</td>\n",
       "      <td>49.794689</td>\n",
       "      <td>6.186345</td>\n",
       "      <td>5.377492</td>\n",
       "      <td>7.577825</td>\n",
       "      <td>1.778187e+05</td>\n",
       "      <td>9.849574e+04</td>\n",
       "      <td>7.951556e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000360e+05</td>\n",
       "      <td>5.020100e+04</td>\n",
       "      <td>4.512600e+04</td>\n",
       "      <td>6.547000e+03</td>\n",
       "      <td>3406.000000</td>\n",
       "      <td>3107.000000</td>\n",
       "      <td>5.699800e+04</td>\n",
       "      <td>3.475100e+04</td>\n",
       "      <td>2.224700e+04</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>762.000000</td>\n",
       "      <td>49.510000</td>\n",
       "      <td>52.270000</td>\n",
       "      <td>46.450000</td>\n",
       "      <td>2.532000e+03</td>\n",
       "      <td>1.703000e+03</td>\n",
       "      <td>8.290000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.261420e+05</td>\n",
       "      <td>6.638400e+04</td>\n",
       "      <td>6.041100e+04</td>\n",
       "      <td>1.363900e+04</td>\n",
       "      <td>7221.000000</td>\n",
       "      <td>6457.000000</td>\n",
       "      <td>9.768700e+04</td>\n",
       "      <td>5.357800e+04</td>\n",
       "      <td>4.391400e+04</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>81.750000</td>\n",
       "      <td>87.280000</td>\n",
       "      <td>75.800000</td>\n",
       "      <td>1.527700e+04</td>\n",
       "      <td>9.289000e+03</td>\n",
       "      <td>6.114000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.841330e+05</td>\n",
       "      <td>9.665500e+04</td>\n",
       "      <td>8.776800e+04</td>\n",
       "      <td>1.944000e+04</td>\n",
       "      <td>10342.000000</td>\n",
       "      <td>9172.000000</td>\n",
       "      <td>1.413290e+05</td>\n",
       "      <td>7.590600e+04</td>\n",
       "      <td>6.383600e+04</td>\n",
       "      <td>922.000000</td>\n",
       "      <td>903.000000</td>\n",
       "      <td>85.970000</td>\n",
       "      <td>91.180000</td>\n",
       "      <td>80.920000</td>\n",
       "      <td>2.395900e+04</td>\n",
       "      <td>1.404900e+04</td>\n",
       "      <td>9.558000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.490330e+05</td>\n",
       "      <td>1.750550e+05</td>\n",
       "      <td>1.700260e+05</td>\n",
       "      <td>3.794500e+04</td>\n",
       "      <td>19982.000000</td>\n",
       "      <td>17954.000000</td>\n",
       "      <td>2.679000e+05</td>\n",
       "      <td>1.455480e+05</td>\n",
       "      <td>1.235030e+05</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>942.000000</td>\n",
       "      <td>89.330000</td>\n",
       "      <td>93.400000</td>\n",
       "      <td>85.400000</td>\n",
       "      <td>5.036700e+04</td>\n",
       "      <td>2.787200e+04</td>\n",
       "      <td>2.086600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.247845e+07</td>\n",
       "      <td>6.736815e+06</td>\n",
       "      <td>5.741632e+06</td>\n",
       "      <td>1.209275e+06</td>\n",
       "      <td>647938.000000</td>\n",
       "      <td>561337.000000</td>\n",
       "      <td>1.023759e+07</td>\n",
       "      <td>5.727774e+06</td>\n",
       "      <td>4.509812e+06</td>\n",
       "      <td>1093.000000</td>\n",
       "      <td>1185.000000</td>\n",
       "      <td>98.800000</td>\n",
       "      <td>99.300000</td>\n",
       "      <td>98.310000</td>\n",
       "      <td>2.221137e+06</td>\n",
       "      <td>1.210040e+06</td>\n",
       "      <td>1.011097e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       population_total  population_male  population_female  \\\n",
       "count      4.930000e+02     4.930000e+02       4.930000e+02   \n",
       "mean       4.481124e+05     2.343468e+05       2.137656e+05   \n",
       "std        1.033228e+06     5.487786e+05       4.848622e+05   \n",
       "min        1.000360e+05     5.020100e+04       4.512600e+04   \n",
       "25%        1.261420e+05     6.638400e+04       6.041100e+04   \n",
       "50%        1.841330e+05     9.665500e+04       8.776800e+04   \n",
       "75%        3.490330e+05     1.750550e+05       1.700260e+05   \n",
       "max        1.247845e+07     6.736815e+06       5.741632e+06   \n",
       "\n",
       "       0-6_population_total  0-6_population_male  0-6_population_female  \\\n",
       "count          4.930000e+02           493.000000             493.000000   \n",
       "mean           4.709285e+04         24849.527383           22243.320487   \n",
       "std            1.050279e+05         55535.310272           49523.241379   \n",
       "min            6.547000e+03          3406.000000            3107.000000   \n",
       "25%            1.363900e+04          7221.000000            6457.000000   \n",
       "50%            1.944000e+04         10342.000000            9172.000000   \n",
       "75%            3.794500e+04         19982.000000           17954.000000   \n",
       "max            1.209275e+06        647938.000000          561337.000000   \n",
       "\n",
       "       literates_total  literates_male  literates_female    sex_ratio  \\\n",
       "count     4.930000e+02    4.930000e+02      4.930000e+02   493.000000   \n",
       "mean      3.461527e+05    1.894384e+05      1.567143e+05   930.294118   \n",
       "std       8.220952e+05    4.534753e+05      3.690677e+05    55.849106   \n",
       "min       5.699800e+04    3.475100e+04      2.224700e+04   700.000000   \n",
       "25%       9.768700e+04    5.357800e+04      4.391400e+04   890.000000   \n",
       "50%       1.413290e+05    7.590600e+04      6.383600e+04   922.000000   \n",
       "75%       2.679000e+05    1.455480e+05      1.235030e+05   971.000000   \n",
       "max       1.023759e+07    5.727774e+06      4.509812e+06  1093.000000   \n",
       "\n",
       "       child_sex_ratio  effective_literacy_rate_total  \\\n",
       "count       493.000000                     493.000000   \n",
       "mean        902.332657                      85.131460   \n",
       "std          49.794689                       6.186345   \n",
       "min         762.000000                      49.510000   \n",
       "25%         868.000000                      81.750000   \n",
       "50%         903.000000                      85.970000   \n",
       "75%         942.000000                      89.330000   \n",
       "max        1185.000000                      98.800000   \n",
       "\n",
       "       effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "count                    493.000000                      493.000000   \n",
       "mean                      89.920162                       79.967181   \n",
       "std                        5.377492                        7.577825   \n",
       "min                       52.270000                       46.450000   \n",
       "25%                       87.280000                       75.800000   \n",
       "50%                       91.180000                       80.920000   \n",
       "75%                       93.400000                       85.400000   \n",
       "max                       99.300000                       98.310000   \n",
       "\n",
       "       total_graduates  male_graduates  female_graduates  \n",
       "count     4.930000e+02    4.930000e+02      4.930000e+02  \n",
       "mean      6.620236e+04    3.771556e+04      2.848680e+04  \n",
       "std       1.778187e+05    9.849574e+04      7.951556e+04  \n",
       "min       2.532000e+03    1.703000e+03      8.290000e+02  \n",
       "25%       1.527700e+04    9.289000e+03      6.114000e+03  \n",
       "50%       2.395900e+04    1.404900e+04      9.558000e+03  \n",
       "75%       5.036700e+04    2.787200e+04      2.086600e+04  \n",
       "max       2.221137e+06    1.210040e+06      1.011097e+06  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
    "\n",
    "The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).  As mentioned on their website: \n",
    ">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "\n",
    "If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib.Pyplot\n",
    "\n",
    "One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are using the inline backend\n",
    "%matplotlib inline \n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*optional: check if Matplotlib is loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.0.3\n"
     ]
    }
   ],
   "source": [
    "print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*optional: apply a style to Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)\n",
    "mpl.style.use(['ggplot']) # optional: for ggplot-like style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting in *pandas*\n",
    "\n",
    "Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
    "\n",
    "Documentation:\n",
    "- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
    "- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line Pots (Series/Dataframe) <a id=\"12\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting in *pandas*\n",
    "\n",
    "Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
    "\n",
    "Documentation:\n",
    "- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
    "- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is a line plot and why use it?**\n",
    "\n",
    "A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
    "Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's start with a case study:**\n",
    "\n",
    "Let us compare the total population of Kerala state to know the trend of population in each district. We can quickly visualize this effort using a `Line` plot:\n",
    "\n",
    "**Question:** Plot a line graph of population from Kerala using `df.plot()`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will extract the data series for Kerala State."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_of_city</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alappuzha</th>\n",
       "      <td>174164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kochi</th>\n",
       "      <td>601574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kollam</th>\n",
       "      <td>349033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kozhikode</th>\n",
       "      <td>432097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palakkad</th>\n",
       "      <td>131019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              population_total\n",
       "name_of_city                  \n",
       "Alappuzha               174164\n",
       "Kochi                   601574\n",
       "Kollam                  349033\n",
       "Kozhikode               432097\n",
       "Palakkad                131019"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl = dt.loc['KERALA',['name_of_city', 'population_total']]\n",
    "kl.set_index('name_of_city', inplace= True)\n",
    "kl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will plot a line plot by appending `.plot()` to the `kl` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd97306a5f8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kl.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*pandas* automatically populated the x-axis with the index values (state_name), and the y-axis with the column values (population). However, notice how the name_city were not displayed properly because pandas took default figure size. Therefore, let's change the figure size for plotting.\n",
    "\n",
    "Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kl.plot(kind='line',figsize=(9,4))\n",
    "\n",
    "plt.title('State-wise Population')\n",
    "plt.ylabel('Population')\n",
    "plt.xlabel('District Name')\n",
    "\n",
    "plt.show() # need this line to show the updates made to the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see the male and female population in cities of kerala"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_of_city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alappuzha</th>\n",
       "      <td>83888</td>\n",
       "      <td>90276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kochi</th>\n",
       "      <td>296668</td>\n",
       "      <td>304906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kollam</th>\n",
       "      <td>168076</td>\n",
       "      <td>180957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kozhikode</th>\n",
       "      <td>206494</td>\n",
       "      <td>225603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palakkad</th>\n",
       "      <td>63833</td>\n",
       "      <td>67186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              population_male  population_female\n",
       "name_of_city                                    \n",
       "Alappuzha               83888              90276\n",
       "Kochi                  296668             304906\n",
       "Kollam                 168076             180957\n",
       "Kozhikode              206494             225603\n",
       "Palakkad                63833              67186"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl_mf = dt.loc['KERALA',['name_of_city', 'population_male', 'population_female']]\n",
    "kl_mf.set_index('name_of_city', inplace= True)\n",
    "kl_mf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kl_mf.plot(kind='line',figsize=(9,4))\n",
    "\n",
    "plt.title('State-wise Population')\n",
    "plt.ylabel('Population')\n",
    "plt.xlabel('District Name')\n",
    "\n",
    "plt.show() # need this line to show the updates made to the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Compare the trend of different total_population(like 0-6, literates, graduates,etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_total</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>total_graduates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_of_city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alappuzha</th>\n",
       "      <td>174164</td>\n",
       "      <td>15434</td>\n",
       "      <td>153269</td>\n",
       "      <td>22263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kochi</th>\n",
       "      <td>601574</td>\n",
       "      <td>52466</td>\n",
       "      <td>535318</td>\n",
       "      <td>115773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kollam</th>\n",
       "      <td>349033</td>\n",
       "      <td>31304</td>\n",
       "      <td>292641</td>\n",
       "      <td>39521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kozhikode</th>\n",
       "      <td>432097</td>\n",
       "      <td>41574</td>\n",
       "      <td>378033</td>\n",
       "      <td>61225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palakkad</th>\n",
       "      <td>131019</td>\n",
       "      <td>11614</td>\n",
       "      <td>112479</td>\n",
       "      <td>16827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              population_total  0-6_population_total  literates_total  \\\n",
       "name_of_city                                                            \n",
       "Alappuzha               174164                 15434           153269   \n",
       "Kochi                   601574                 52466           535318   \n",
       "Kollam                  349033                 31304           292641   \n",
       "Kozhikode               432097                 41574           378033   \n",
       "Palakkad                131019                 11614           112479   \n",
       "\n",
       "              total_graduates  \n",
       "name_of_city                   \n",
       "Alappuzha               22263  \n",
       "Kochi                  115773  \n",
       "Kollam                  39521  \n",
       "Kozhikode               61225  \n",
       "Palakkad                16827  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl_to = dt.loc['KERALA',['name_of_city','population_total', '0-6_population_total', 'literates_total', 'total_graduates']]\n",
    "kl_to.set_index('name_of_city', inplace= True)\n",
    "kl_to.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEaCAYAAAA8FXKrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd8zdf/wPHXuRk3OzJtsfdIiE1CJFQpvlS1pUWp9f1+rWqrFG2RqtYo+q0W1er0taK2xAiqZiSIEiM2QRIhe3zO74/83G9Dxs0kcZ6Ph0ebzz2fc97340pOzngfIaWUKIqiKIqiKEVC97QDUBRFURRFKUtU50pRFEVRFKUIqc6VoiiKoihKEVKdK0VRFEVRlCKkOleKoiiKoihFSHWuFEVRFEVRipDqXCmK8swbMmQIvr6+TzuMElW9enVmzZpV6Hq+//57TE1NiyAiRVGMpTpXilJGJCUlMW3aNOrUqYOlpSVOTk60bNmSRYsWGcoMHz6cTp065bvuAwcOIITg8uXLRRdwPnz55ZesWbOmWNv4/vvvEUIY/pQvX56ePXty6tSpYm23qFy/fh0hBHv37s1yfcCAAdy4cePpBKUozyn164yilBGjR49mz549fPnllzRr1owHDx5w4sQJrl69+rRDKzR7e/sSacfExITr168DEBkZybhx4+jWrRt//fVXicVQ1CwtLbG0tHzaYSjK80UqilIm2Nvby8WLF+f4+owZMySQ5c/KlSullFIuXLhQNmvWTFpbW8vy5cvLAQMGyJs3b0oppYyMjHziPm9vb0O9v/76q2zWrJnU6/XSzc1NTpgwQcbHx+cYR0JCgjQ3N5eBgYGGa15eXtLc3FwmJCRIKaVMSkqSer1ebtmyRUop5eDBg2WXLl0M5U+fPi27du0q7e3tpZWVlaxfv75ctWqV4fWHDx/KsWPHykqVKklLS0vp7u4u161bl+vzW7lypTQxMcly7cCBAxKQ27dvl1JK+eDBAzlixAjp7Ows9Xq9bNGihdyxY4eh/KNntWrVKunj4yMtLCxk9erV5U8//fREmf3792dpq1atWnLGjBmGr93c3OTMmTMNX//888+yVatW0s7OTjo5OckXX3xRnjt3zvD6439Hbm5uOb6vLVu2yObNm0tzc3Pp4uIiR48eneXv7NHz/uabb2S1atWkra2t7NWrl7xz506uz1BRlExqWlBRyoiKFSuyfft2YmJisn190qRJvP7667Rt25Zbt25x69YtBgwYYHj9iy++4NSpU2zYsIGrV6/y6quvAlC1alU2btwIwJEjR7h16xbr168HMqfSRo8ezTvvvMOZM2dYtWoVQUFBjBo1Ksc4raysaN26Nbt27QIypzMPHTqEnZ0d+/fvB+CPP/4gIyODjh07ZlvHa6+9hpOTEwcPHuTUqVPMnz8fBwcHAKSUvPTSS4SFhbF69WpOnz7N6NGjefXVVw1tGuvRiE9aWhoAb731Fjt27OCnn37ixIkTtG/fnp49e3L27Nks973//vu89dZbhIaGMnDgQN544w2OHTuWr7Yfl5KSwrRp0wgJCSEwMBATExN69OhBamoqACEhIQCsW7eOW7ducfTo0WzrOXnyJL169cLLy4vQ0FB++OEHNm/e/MTf2dGjR9mzZw9btmxh+/bthIaGMmnSpEK9B0V5bjzt3p2iKEXjwIEDslq1alKn08kmTZrIt99+WwYEBEhN0wxlhg0blmXUKSchISESkNevX5dSSrl//34JyMjIyCzl3Nzc5Ndff53lWnBwsARkTExMjvXPmDFDtmzZUkop5c6dO2XNmjXl6NGj5bvvviullHLKlCmybdu2hvKPj1zZ2dkZRt0et2fPHqnX6+X9+/ezXB86dKjs3bt3jjE9PsJz584d2bNnT2lnZyejoqLk+fPnJWAYTXvEw8NDDh06VEr5v1GpDz/8MEuZtm3byoEDB2Ypk9+Rq8dFR0dLQB44cEBKKeW1a9ckIPfs2ZPr+xo0aJDh2T8SEBAghRDy8uXLUsrM5+3s7CyTk5MNZT799FNZoUKFHONRFOV/1MiVopQR7du35+LFi+zfv5/BgwcTFRVFv3796NWrFzKP89n37t1Lt27dqFq1Kra2tnTo0AGAK1eu5HjP3bt3uXLlChMnTsTGxsbwp3v37gBcuHCB/fv3Z3nN398fAB8fH0JCQoiLi2P37t106dKFzp07s3v3bgB2796Nj49Pjm1PmjTJsDj/o48+MozaQOaIS2pqKpUrV87S9k8//cT58+dzfQ4ZGRmG8q6urly4cIG1a9fi6urKmTNnAPDy8spyj5eXF+Hh4VmutW3bNsvX7du3N9xfUKGhofzjH/+gRo0a2NraUq1aNSD3v6PshIeHP/EevL29kVJmibFBgwbo9XrD15UrVyYqKqoQ70BRnh9qQbuilCGmpqa0a9eOdu3a8c477/DTTz/xxhtvsG/fPry9vbO95+rVq7z44ou88cYbTJ8+HWdnZ65fv46vr69hyik7mqYBmTv5Onfu/MTrVapUQUpJaGio4ZqjoyMAbdq0Qa/Xs3fvXnbv3s2ECRPo3Lkzr732GlevXuXYsWPMnj07x7anTZvGwIED2b59O7t378bf35/33nuPWbNmoWka9vb22U6LmZub51gnZC5oDw0NRQiBq6srtra2uZaHzGlIIUSeZR7R6XRPXIP/TT1mJzExka5du9KhQwe+++47KlSoAECjRo1y/TvKSU7x/v36489KCJFnJ11RlEyqc6UoZViDBg0AuHPnDpD5AzMjIyNLmaNHj5KUlMTChQsNa4yOHz+epcyjH7R/v7d8+fJUrVqVc+fO8fbbb+cYQ+3atZ+4Zm5uTvv27dmwYQMhISH4+Pjg7OxMo0aN+OSTTzAxMaFdu3a5vreaNWsyZswYxowZw5w5c/j888+ZNWsWnp6e3L9/n+TkZBo3bpxrHcbGC5kdGYB9+/bx4osvGq7v378fDw+PLGUPHTqUpcyff/5p+LtwcXEB4ObNm4bX79y5k2u6hL/++ou7d+8ye/ZsQz0HDx7M0tnJ7u8op/cRHByc5VpwcDBCCBo2bJjrvYqiGEdNCypKGeHt7c3SpUs5duwYV65cYdeuXYwZM4Zy5coZRpZq1KjB2bNnCQ8P5969e6SkpFCnTh2EEMybN4/IyEgCAgL45JNPstTt5uaGTqdj69at3Llzh7i4OABmz57NokWLmDVrFqdPn+bcuXMEBAQwcuTIPOP18fHh559/pn79+ri6uhqu/fDDD7Rr1w4LC4ts74uPj+ef//wnu3fvJjIykhMnTrB9+3ZDx8DHxwdfX1/69u3Lhg0buHTpEsePH2fx4sUsW7aswM+3Vq1a9O/fnzFjxrBjxw7Onj3LuHHjOH36NO+++26WsitWrOCXX34hIiKC6dOn8+effzJ+/Hggc5F8+/btmTt3LmFhYRw/fpw333wzyxTc49zc3NDr9SxevJiLFy+ya9cuxo0bl2WkydnZGRsbG3bu3Mnt27eJjY3Ntq53332XkJAQJk6cyNmzZ9m+fTv//ve/GThwoGGqUVGUQnp6y70URSlKn376qezQoYN0cXGRer1eVq1aVQ4cOFCGh4cbykRHR8vu3btLOzu7LKkYlixZIqtUqSItLCxk+/bt5bZt255YHP3ZZ5/JSpUqSZ1Ol2VR/IYNG2SbNm2kpaWltLW1lc2aNZMff/xxnvEeOXJEAnLs2LGGa7///rsEnljI/fcF7UlJSfK1116T1atXl3q9Xrq4uMhXXnlFXr161VA+MTFRvv/++7J69erSzMxMli9fXnbr1k3u2rUrx3iyS1nwuLi4OEMqBnNz81xTMXh7exvSU/w9TYSUUp47d056eXlJKysrWbt2bblu3bo8F7SvWbNG1q5dW+r1eunu7i737t0rTUxMsizs/+GHH2T16tWlqamp0akYnJ2d5ahRo7JNxfB3P/74o1Q/MhTFOEJKNYmuKIpSFC5fvkyNGjXYv3+/YVOAoijPHzUtqCiKoiiKUoRU50pRFEVRFKUIqWlBRVEURVGUIqRGrhRFURRFUYqQ6lwpiqIoiqIUIZVENAd/T/CnFI6zszP37t172mGUCepZFi31PIuWep5FRz3LolWpUqUSbU+NXCmKoiiKohQh1blSFEVRFEUpQqpzpSiKoiiKUoTUmitFeU5IKUlOTkbTtCxn0j3PoqKiSElJedphPBOklOh0OiwsLNTnQ1EKSXWuFOU5kZycjJmZGaam6p/9I6amppiYmDztMJ4Z6enpJCcnY2lp+bRDUZRSTU0LKspzQtM01bFScmVqaoqmaU87DEUp9VTnSlGeE2qqRzGG+pwoSuGpzpWiKIqiPEPkwwckBe9AnU5XeqnOlaIoiqI8Q+Rv3/Jg4cfI3ZufdihKAanOlaIoZca1a9fw8fHJs8yGDRsMX4eFhTFt2rQiiyEuLo7vv/8+z3KPx5Fbubzek1J2yJtXkUf3I6yskWtXIq9HPu2QlAJQnStFUZ4rj3dqmjVrxsyZM4us/gcPHrBq1ap8x6EoAHLzajC3wPGzZWBti/btF0iVLqTUUVuHFOU5pP22DHmtaH8jFlVroHv17VzLXLt2jYEDB+Lh4UF4eDg1atRg0aJFHDt2jJkzZ5KRkUGzZs349NNP0ev1tG7dml69enHw4EEAlixZQo0aNRg/fjy+vr707NkTgDp16nD+/Pkn2ho7diyJiYkAzJo1i5YtW+Lv78+FCxfw8/NjwIABNGzYkKVLl7Jq1SpiY2N55513uHr1KhYWFsydO5eGDRsyb948bty4wdWrV7lx4wbDhw9n2LBh2b5Hf39/rly5gp+fH15eXnz44YfMmjWLPXv2IIRg7Nix9O7dO0sc/fv3p3v37tnGqzw/5I0ryGMHEC/0w7RKdXRvjUdbMAO59jvEwNFPOzwlH9TIlaIoJerixYsMGjSIoKAgbG1t+eabb5gwYQJff/01u3btIj09PcvIj42NDVu2bGHIkCHMmDHD6HacnZ359ddf2bFjB19//TXTp08HYMqUKbRq1YrAwEBGjRqV5Z558+bRuHFjgoKCmDx5MuPGjTO8duHCBX7++We2bNnC/PnzSUtLy7bdKVOm4ObmRmBgINOmTWPr1q2Eh4cTGBjIb7/9xqxZs4iKisoSx4gRI3KMV3l+aJt+Bb0FomsfAERDD0TXPsi925Chh55ydEp+qJErRXkO5TXCVJwqVapkGJHp27cvCxcupFq1atSqVQuA/v3788MPP/D225kx9unTx/Dfjz76yOh20tLSmDp1KmfOnEGn03Hp0qU87zly5AjLli0DoEOHDsTGxvLgwQMAunTpgl6vR6/X4+zszN27d6lUqZJRdfbp0wcTExNcXFxo06YNYWFh2NjYFDpepeyQ1yPh+EFEj1cQNnaG66LPG8izJ9F+WIyueh1EOaenGKViLDVypShKicpvHqW/l3/0/39PdimlzHYUadmyZbi4uBAYGMi2bdtyHGn6u+y2vj9qU6/XG66ZmJiQkZFhVPzGbqcvSLxK2aFt+g0srRB+fbJcF2Zm6N6eBKmpaN8tRKokr6WC6lwpilKibty4wbFjxwDYuHEjHTt25Nq1a0RGZq4BW7duHW3atDGU//333w3/bdGiBQBVqlTh1KlTAOzYsSPbjsiDBw9wdXVFp9Oxbt06Q2fIxsaGhISEbGNr06YN69evB+DgwYM4Ojpia2ubr/dnbW1NfHx8ljp///13MjIyiI6O5vDhw7i7uz8RR07xKmWfvHoJQv5EdOmFsLZ54nVRoQri1bfhrzBkYMBTiFDJLzUtqChKiapTpw5r1qxh8uTJ1KhRg08++YTmzZszcuRIw4L2N954w1A+NTWVnj17omkaX331FQADBw5k6NCh9OjRgw4dOmBlZfVEO4MHD2bEiBFs3ryZ9u3bG8o0aNAAExMTfH19efXVV2nYsKHhnokTJzJx4kR8fX2xsLBg4cKF+X5/jo6OtGzZEh8fHzp37syHH37I8ePH8fPzQwjB1KlTcXV1xcHBwRDHK6+8kmO8StmXOWpljfDrlWMZ0cEPeToEueFHZL0miOp1Si5AJd+EVClgs3Xz5s2nHUKZ4ezszL179552GGVCYZ5lYmLiU/+Bfe3aNQYPHszu3buNKt+6dWu2bduGo6NjscRjampKenp6sdRdWhXmc6L+reefvHIRbdYERK/X0b30quF6ds9SJjxE+3gcmJmjm7YAYaEO2DaWMesji5KaFlQURVGUp0Tb9CtYWSO6vJRnWWFti27YBLh7C/nbshKITimoEpkWvHnzJgsWLDB8fefOHV555RW8vb1ZsGABd+/excXFhQkTJmBjY4OUkpUrV3LixAn0ej1jxoyhZs2aAOzdu9ewJqJv37506tQJgEuXLvHVV1+RmpqKh4cHQ4cORQhBfHx8tm0oilLyqlatavSoFcDhw4eLMZrCiYmJYcCAAU9cX716dbGNtClli7x8HsKOIHoPRFhZG3WPqNcE0f1l5NY1yMbNEZ4dijlKpSBKpHNVqVIlPv/8cwA0TWPkyJG0atWKgIAAmjRpQp8+fQgICCAgIIBBgwZx4sQJbt++zaJFizh//jzLly/H39+f+Ph41q5dy5w5cwCYPHkynp6e2NjYsGzZMkaOHEmdOnX49NNPCQ0NxcPDI8c2FEVRCsPR0ZHAwMCnHYZSimm//wrWtkaNWv2deOk15F9haD9+ha5GPYSTSzFFqBRUiU8Lnjp1igoVKuDi4sLRo0fx9vYGwNvbm6NHjwJw7NgxvLy8EEJQt25dEhISiI2NJTQ0lKZNm2JjY4ONjQ1NmzYlNDSU2NhYkpKSqFu3LkIIvLy8DHXl1IaiKIqiPC0yMgJOHUN07YOwzN8aN2Fqim74O5Choa2Yh9TUztJnTYnvFvzjjz9o3749kHnAqYODAwAODg6GZH0xMTE4Ozsb7nFyciImJoaYmBicnP6XQM3R0THb64/K59bG44KCgggKCgJgzpw5WdpXCsfU1FQ9zyJSmGcZFRWFqanaIPw49UyyepQktSDUv3Xjxf7HnzRbe5z7v4nO8skpwTyfpbMzSaMm8eDLmVgGb8Wm/9BijFbJrxL9rpKens7x48d5/fXXcy2XWyK/7K4XxYZHX19ffH19DV+rHS9FR+0gKjqFeZYpKSmYmJgUcUSlm9ot+KSUlJQCf8bUv3XjyItn0U4cQvQdTExCEiQkPVHGmGcpG3kiWnmR8NsKkqrVQdSqX1whl3plerfgiRMnqFGjBuXKlQPA3t6e2NhYAGJjY7Gzy0z57+TklOVDFR0djYODA46OjkRHRxuux8TE4ODggJOTU5br0dHRhgWlObWhKIqiKE+D9vuvYGOH6PxioeoRQmQe6OzgjLZ8HjIpsYgiVAqrRDtXf58SBPD09CQ4OBiA4OBgw3ljnp6e7Nu3DyklERERWFlZ4eDggLu7O2FhYcTHxxMfH09YWBju7u44ODhgaWlJREQEUkr27duHp6dnrm0oivJ07Nmzh44dO9K+fXuWLFmSbZkbN27w2muv4e3tTadOnbh27VoJR5lV69atDUsNcrJo0aIsX/fqlXNCyIJYtmwZSUlPjnDkFUdOjHlPStGTF87AmROIF/oVSZ4qYWWduf4q+i7yl6VFEKFSFEqsc5WSksLJkydp3bq14VqfPn04efIkY8eO5eTJk4YDWj08PHB1dWXs2LF88803DB8+HMg8tqJfv3588MEHfPDBB7z88suGtArDhw/nm2++YezYsZQvXx4PD49c21AUpeRlZGQwdepUfvrpJ/bs2UNAQAARERFPlBs3bhyjR48mODiYLVu2lIp1PIsXL87y9aNje4rK8uXLjepcPR6H8mzRfv8VbO0RnboXWZ2idgPES68iD+1FO7SnyOpVCq7E1lzp9Xq+++67LNdsbW2ZPn36E2WFEIYO1eN8fHzw8fF54nqtWrWYN2/eE9dzakNRnmfLj0URGZtcpHXWcLBguGf5XMucOHGC6tWr4+bmBkDv3r3ZsWMHdevWNZSJiIggPT0dLy8vIPOsvty0bt2aXr16cfDgQQCWLFlCjRo1uH79OhMnTiQmJgZHR0cWLFhA5cqVGT9+PHq9noiICO7du8f06dPx8/Nj9erVnDx5ktmzZwPw5ptvMmrUKNq1a5elvbfeeoubN2+SkpLCsGHDGDRoEP7+/iQnJ+Pn50e9evVYsmQJderU4fz580gpmTVrFnv27EEIwdixY+nduzcHDx5k/vz5ODg4cO7cOZo2bcrixYuzXV+6YsUKoqKi6N+/Pw4ODqxdu5aAgAAWL16MlJIuXbowderUbOPILl7l6ZAR4fBXGKL/Wwi9RZHWLV7sjzwTivx5KbJWA4RLhSKtX8kflaFdUZQSc/v27SwLSytWrMjt27ezlLl06RJ2dnYMHz6crl27MnPmzDwPMbaxsWHLli0MGTKEGTNmADB16lRefvllgoKC6Nu3L9OmTTOUv379OuvWrePnn39m8uTJJCcb39GcN28e27dvZ+vWrXz33XfExMQwZcoULCwsCAwMfGKqc+vWrYSHhxMYGMhvv/3GrFmziIqKAuD06dN8/PHH7N27lytXruSYKmbYsGGUL1+eNWvWsHbtWm7fvs3s2bP573//y86dOwkNDWX79u3ZxpFdvMrTof3+C9iVQ3gX3ajVI8LEBN3wiSB0meuv1EaNp0rtQVaU51BeI0zFxZidwOnp6Rw5coQdO3ZQuXJlRo8ezX//+19ee+21HOt9NN3fp08fPvroIwCOHz/O8uXLAejXrx+zZs0ylH/ppZfQ6XTUrFkTNzc3Lly4YPR7+O6779i2bRuQefpEZGRkrhnZjxw5Qp8+fTAxMcHFxYU2bdoQFhaGjY0N7u7uhs5mo0aNuHbtGq1atcozhrCwMNq2bWtIQdO3b18OHTrECy+8UOh4leIhz52Gc6cQA4Yh9PpiaUM4uSIGjUYu+wK5ZTWi98BiaUfJmxq5UhSlxFSsWDHLoei3bt3C0dERPz8//Pz82LlzJxUrVqRx48a4ublhampKt27dOHXqVK71/r2DllvalpzKCCEwNTVF0zTDtZSUlCfqOHjwIPv372fTpk0EBQXRuHHjbMv9XW6pYszNzQ3/b2JiYnRaCGPTzxQkXqXoSSnRfv8Z7B0RXk92gIuSrpUXol0X5JY1yIjTxdqWkjPVuVIUpcS4u7sTGRnJ1atXSU1NZePGjfTo0YPAwEACAwPp2rUr7u7u3L9/35Be5Y8//siyJis7jxaP//7777Ro0QLI3Cm8ceNGANavX59lRGjz5s1omsbly5e5cuUKtWrVomrVqoSHh6NpGjdu3CA0NPSJdh4+fIi9vT2WlpZcuHCBkJAQw2tmZmakpaU9cU+bNm34/fffycjIIDo6msOHD+Pu7p7PJ5c59RkfHw9kbvo5dOgQMTExZGRkEBAQQNu2bZ+II7d4lRJ09iREhCO6v4wwL55Rq78Tr70NLuXRVsxHJsQXe3vKk9S0oKIoJcbU1JRZs2bx+uuvo2kaAwYMoF69elnKmJiYMH36dAYMGICUkiZNmuSZeDg1NZWePXuiaRpfffUVADNnzmTixIksXbrUsKD9kZo1a9KvXz/u3bvHnDlzsLCwoGXLllSrVo0uXbpQr149mjRp8kQ7nTp14scff8TX15eaNWvSvHlzw2sDBw7E19eXJk2aZFl31b17d44fP46fnx9CCKZOnYqrq2u+piIf1T9o0CBcXV1Zu3YtH3zwAf3790dKiY+PD926dXsijnnz5uUYr1IyMketfoVyTgivriXSprCwQjd8Etpn76H9uATdyPdzHNFVioeQRZHevAz6+9SFUjgqa3PRKcyzTExMxMoqf2eYlQatW7dm27ZtRq8jGj9+PL6+vvTs2VNlaM9GYT4n6t/6k+SZULQF0xGvj0TXuYfR9xXFs9S2rUWuX4UY/G90HfwKVVdpV6YztCuKoijK8yJz1OoXcHBGdCiZUau/E93+AfWaIH/9Fnn7Rom3/zxT04KKopQKw4YN4+rVq1muTZ06lcOHD+ernoULFxZlWEUup/fZqVOnpxOQUnDhJ+DiWcTA0QgzM6NvS0nRuBjxEDsHWajpPKEzQffWBLRPxqEtn4du8mcIU+PjUApOda4URSkVVqxY8bRDKBHPy/ss6wyjVo4uiA6++br35NEkbt94QJ2Geuo3KdwROcLRGd2b/0L7+lNkwM+Il4cUqj7FOGpaUFEURVGK2ukQiIxA9Oifr9GiO7fTuH0jDTt7M86fSeHqpcKnzhDN2yK8XkDuWI888+QuWKXoqc6VoiiKohQhw6iVkyuiXRej79M0SXhIEtY2OnoNqIpzeVNOHkviXtSTKT7yS7wyDCpUQftuIfLhg0LXp+ROda4URVEUpSidOgaXzyN6vJKvUavI8ynEP9Ro5GGJmZkOz3bW2NjqOPpHAg8f5H4EVF6EXo/u7UmQ8ADth0VGJ6JVCkZ1rhRFURSliBjyWrlUQLT1Mfq+5CSNiNPJuFY0pXylzA6ZmbmglZcNJiaCw/sSSEnW8qgld6JaTUTfwRB2BBm8vVB1KblTnStFUUpMnTp1gMwDnN9++20g8/DiXbt2FVkbcXFxfP/990VWH8CyZctISkrKs9yiRYuMqq9169bqAOWyKuwIXLnw/6NWxu8ZO3sqmQwNGnlkXcBuZa2jVQdrUpI1juxPICO9cCNOostL0MgD+d8VyJtX875BKRDVuVIUpcRVqFCBZcuWARAeHs7u3bvzdX9uiT8fPHjAqlWrChXf45YvX25U52rx4sVF2q5Sukgp0Tb9/6hVm85G3xcbnc61yFRq1tVjY2vyxOvlnExp3saK+zEZnDicWKgpPaHToRs6Hiws0ZZ9gUxLLXBdSs5UKgZFeQ6dDknkwf3CreF4nF05Exo3Ny6z97Vr1xg8eDDbt2/niy++IDk5mSNHjvCvf/0LPz8/PvzwQ86ePUt6ejrvvPMO3bp1Y/Xq1ezatYuUlBQSExP5/vvvGTp0KHFxcaSnp/Pee+/RrVs3/P39uXLlCn5+fnh5eTFt2jS+/vprNm3aRGpqKi+88AKTJk0iMTGR0aNHc+PGDTRNY9y4cfTu3fuJWFesWEFUVBRQUeOiAAAgAElEQVT9+/fHwcGBtWvXEhAQwOLFi5FS0qVLF6ZOnYq/vz/Jycn4+flRr149lixZwltvvcXNmzdJSUlh2LBhDBo0qEifufKMCT0MVy8hho5HmDzZScqOlJLTIUnoLQR1G1rkWK5iFXMaumucCU3m7MlkGjQreIoGYe+Abug4tEWfINf9gHj17QLXpWRPda4URXlqzM3NmTRpEidPnmT27NkAfPrpp7Rv35758+cTFxdHjx496NixIwDHjx8nKCgIBwcH0tPTWbFiBba2tsTExPDSSy/RtWtXpkyZwrlz5wgMDAQgODiYyMhItmzZgpSSIUOGcOjQIaKjoylfvjw//PADkDnilZ1hw4bx7bffsmbNGhwdHbl9+zazZ89m+/bt2Nvb89prr7F9+3amTJnCypUrDe0CzJs3DwcHB5KSkujRowcvvvii0cf0KKWL1LTMHYKulRCtvY2+7/rlVO7HZODe2gpTs9wThtasqyfhocaFsylY2ehwq1XwQ6BFE0+ET0/krk3IRh6IJp4Frkt5kupcKcpzyNgRpqdh3759BAYGsnTpUgBSUlK4cSPz6A4vLy8cHByAzN/458yZw+HDhxFCcPv2be7evftEfcHBwQQHB9O1a+bxI4mJiURGRtKqVStmzpzJ7Nmz8fX1pXXr1kbFFxYWRtu2bXFycgKgb9++HDp0iBdeeOGJst999x3btm0DMs8rjYyMVJ2rsurEIbh+GTFsgtGjVmmpkr9OJuPgZEIVt7x3FQohaNzcksQEjVPHk7Cy1uFSoeAZ18XLQ5DnTqGt/BLdR4sQdg4FrkvJqsQ6VwkJCSxdupRr164hhGD06NFUqlSJBQsWcPfuXVxcXJgwYQI2NjZIKVm5ciUnTpxAr9czZswYatasCcDevXtZv349kPlN7dGREJcuXeKrr74iNTUVDw8Phg4dihCC+Pj4bNtQFOXZJKXk22+/pXbt2lmuh4SEZDlQeP369URHR7Nt2zbMzMxo3bo1KSlPJlyUUvKvf/2LN95444nXAgMD2blzJ59++ine3t5MmDDBqPiMcfDgQfbv38+mTZuwtLTk5ZdfzjY+pfSTmpa51qpCZUQrL6PviziTTEqypFVHS6OPudHpBC3aWXNw10OOHUygvY8tduWM68w9TpiZo3t7EtrsdzI7WP+ejtCppdhFocSe4sqVK3F3d2fhwoV8/vnnVK5cmYCAAJo0acKiRYto0qQJAQEBAJw4cYLbt2+zaNEiRowYwfLlywGIj49n7dq1+Pv74+/vz9q1a4mPjwcyd/OMHDmSRYsWcfv2bUJDM7PQ5tSGUjLknVsqn4qSKxsbG8O/YwBvb29Wrlxp+NycPn062/sePnyIs7MzZmZm/PHHH1y/fh0Aa2vrLPV16tSJ1atXk5CQAMCtW7e4d+8et2/fxtLSkn79+jFq1ChOnTplVIweHh4cOnSImJgYMjIyCAgIoG3btgCYmZmRlpZmiM/e3h5LS0suXLhASEhIQR+R8qwLOQg3riB6vorQGdfRefggg8iIFKrVNKecY/7GOczM/pei4cj+eJKTCp6iQVR2Q/R/C06HIHdvLnA9SlYl0rlKTEzkr7/+wscnM+eHqakp1tbWHD16FG/vzLlpb29vjh49CsCxY8fw8vJCCEHdunVJSEggNjaW0NBQmjZtio2NDTY2NjRt2pTQ0FBiY2NJSkqibt26CCHw8vIy1JVTG0rx0/ZtR5s6krjPP0Qm573TSnk+tWvXjvPnz+Pn58fGjRsZP348aWlp+Pr64uPjw9y5c7O9r2/fvoSFhdG9e3c2bNhgGOlydHSkZcuW+Pj4MHPmTLy9venTpw+9evWiS5cujBgxgvj4eM6ePcsLL7yAn58fixYtYty4cTnGOHDgQAYNGsTLL79M+fLl+eCDD+jfvz9+fn40adKEbt26Gcr5+vryr3/9i06dOpGRkYGvry9z586lefPmRf/wlKdOahmZea0qVkW07GDcPVISfiIJE1Oo3yTnRey5sbTS0aqjNakpkiP7E0gvRIoG0ak7NGuFXPc98lpkgetR/kfIEhhWuHz5Mt988w1VqlThypUr1KxZkyFDhjBq1Kgs+WiGDh3KypUrmTNnDn369KF+/foAfPLJJwwcOJDw8HDS0tLo168fAGvXrsXc3JxGjRrxyy+/MG3aNAD++usvNm7cyOTJkxkyZEi2bTwuKCiIoKAgAObMmUNqqtqeWhjpVy4S/d4wTFwqkHHrOqZVa2A/eQ6mFSo/7dBKNVNT01zTEOQmKioKvb7gC2CV50NKSgrly5cv0L2F+XyWVsn7A4mbPwP7dz7BwsgDmq9cimf3ttu07uBMw2blsi1j7LO8GpnArq23qFbTms7dKqDTGTe9+DgtLpboCW8irG1x+uI7hL5gnb5nlbm5eYm2VyJrrjIyMoiMjOStt96iTp06rFy5Mtfpuez6eznNRwshimTaydfXF1/f//3DuHfvXqHrfF7JlBS0z6aAhRVy4kzKPYjh/ufTiH5nKLqR7yIaejztEEstZ2fnAn82U1JSMDFyoe3z4nnsDOQlJSWlwJ+xwnw+SyOpZaD9sgwqVeNh3abEG/HeMzIkh/Y9xNZOh3PFtByfl7HP0so2M/Fo+IkE9u++TiP3gqdoYMhYMhbM4O7Xc9ENGlPwep5BlSpVKtH2SmRa0MnJCScnJ0N25jZt2hAZGYm9vT2xsbEAxMbGYmdnZyj/9w9VdHQ0Dg4OODo6Eh0dbbgeExODg4MDTk5OWa5HR0cbduTk1IZSfOTqZXD7OrphExF2DujdW6ObOg8cnNAWfoy2c4Nah6U8k4YNG4afn1+WP3v37n3aYSnPKHn0QOb3ul6vGb0Q/OK5FBITNBo1tyzwKNPjatQxp3ptcy6dS+HyhYJvmhANPRBd/4EM3o4MPVQksT2vSqRzVa5cOZycnLh58yYAp06dokqVKnh6ehIcHAxkbpdu2bIlAJ6enuzbtw8pJREREVhZWeHg4IC7uzthYWHEx8cTHx9PWFgY7u7uODg4YGlpSUREBFJK9u3bh6enp6Gu7NpQiod2dD9y/07EC/0QDd0N14VrRXST54JHa+Salcjl85Fq55TyjFmxYgWBgYFZ/jzakawofyczMpCbfoMq1cGjrVH3JCVqXDiTTMUqZriUL3gKhccJIWjkYYlrRVNOhyRx51Zawev6xyCoVgvt+8XI2Oi8b1CyZfLRRx99VBIN1ahRg6+++ort27eTlpbGm2++Sf369dm4cSPr1q3j4cOHDB06FHNzcypUqEBERATff/89oaGhjBw5EkdHR8zNzbG0tGTJkiXs2rWLfv36Ua9ePUP9S5cuZfPmzdSuXZsXX3wRIQQ1a9bMto28PHz4sLgfSZkj795GLp4JbrXQDR1v+E3OysqKxMREhKkZwrMDmJgid29Gnj6GaNwcYWX9lCMvPR49y4JIS0vDzKzovqGXBTqdDk0r3GG4ZU1hPieF+XyWNvJwMBzYiW7gKESlakbdc/JYIvEPNVp1tMbMPPexjfw+SyEEFSqZEXUrnasXUyhfyQy9Rf7HT4TOBFGvMXLPVuTl84g2nYxOE/Ess7W1LdH2SmRBe2n0aJRNMY5MT0ebOxmibqCbthDh/L8FsdmtHZBhR9FWzAMTU3Sj3kfUa1LSIZdKhVnTkpiYmCVPlKLWXGWnMJ+T52XNlczIQJs+Bswt0E1bYNSUYPSddA7uiaduIz31Gue9LqqgzzIpUeNAUObgQEc/WywsCzZBpe3fiVy1BNFvMLoX+hWojmdJmVxzpZR9MuAniIxA9+a/s3SsciKatUQ35QuwsUWbPw1t92a1DktRlFJBHt4Ld24ZvdZK0ySnQxKxtBLUql+8u/AepWhISytcigbRwQ+at0MG/IS8fL6Ioyz7VOdKKTR5OgS5Yz2iU3dEi3ZG3ycqVEH3wRfQxBP567fIHxapE9oVRXmmyYwM5ObVUK0muBt3ZNLVS6k8iNNo6G6JqWnxT7HZO5jSoq01cfczCPkzAanlv4MlhED35j/BzgFt2TyVqzCfVOdKKRR5PwbtuwXwKMtvPgkra3RjpiB6vor8Yxfa51PUIsoyKi4uLkvOuexcu3aNDRs25FnXtWvXDEmJS0Jh21u2bBlJSeqHU1kgD+2Bu7fR9XrdqLVIqSkaZ08l4+xqSsUqJbfmsXwlMxp7WBJ1M53wsOQC1SGsbdENmwh3byF/W1bEEZZtqnOlFJjUtMyOVUoSuhHvIswLlqBS6HToer+ObvQHcPMa2qwJyAtnijha5Wl78OABq1atyrWMsZ2ropKRkVEi7Sxfvlx1rsoAmZ6eOWrlVhuaGrfz/OypZNLTJI08jD8/sKjUqKOnRl09kREpRJ4v2O5sUa8xont/5B9BaEcPFHGEZVeJHdyslD1y+zr4Kwzx5r+M3i2TG9G8LbryldH+Mxvtiw8Rr49A5/VCEUSqPG7fvn3cvXu3SOt0cXHByyvnQ2v9/f25cuUKfn5+hnJ79uxBCMHYsWPp3bs3/v7+XLhwAT8/P/r370/37t0ZO3asYdfUrFmzjEqnkpSUxPjx47l48SK1a9fm+vXrzJ49m2bNmlGnTh1GjBhBcHAwH3/8Mfv27SMwMJDk5GQ8PT357LPPEEJw8uRJJk6ciKWlJa1atTLUvXr1ak6ePMns2bMBePPNNxk1ahTt2rVj8uTJhIWFkZycTI8ePZg0aRIrVqwgKiqK/v374+DgwNq1awkODuaLL74gNTUVNzc3FixYgLW1Nf7+/uzcuRNTU1O8vLyYPn16Yf5KlCIm/9wN96LQvTbCqI5SXGw6Vy6lUqO2eYEPVy6sRs0sSIzP4PSJJKysdZSvlP/RM/HSq8i/QpE/foWsWRfh5FoMkZYtauRKKRB54S/kxp8RLTtmLnwsIqJyNXRT5kGDpsgf/4P243+Q6QXP2aI8O6ZMmYKbmxuBgYE0b96c8PBwAgMD+e2335g1axZRUVFMmTKFVq1aERgYyIgRI3B2dubXX39lx44dfP3110Z3Nn744Qfs7e0JCgpi/PjxnDx50vBaYmIi9erVY/PmzbRu3ZohQ4awdetWdu/eTVJSEoGBgQBMnDiRmTNnsmnTJqPf4/vvv8+2bdsICgri0KFDnDlzhmHDhlG+fHnWrFnD2rVriYmJ4csvv2T16tXs2LGDZs2a8e233xIbG8u2bdvYs2cPQUFBuZ51qJQ8mZ6G3PJfqFEXmnjmXV5KTp9IwtxcULfx0ztKRugEzdtaY1/OhON/JhAXm//dscLUFN3wd0DT0JbPR2olM+JbmqmRKyXfZEI82rIvwMkVMWhMkQ91C2sbdP+ehtzwE3L7OuTNK+hGTUbYOxRpO8+z3EaYSsKRI0fo06cPJiYmuLi40KZNG8LCwrCxsclSLi0tjalTp3LmzBl0Oh2XLl0yuv5hw4YBUL9+fRo0aGB4zcTEhB49ehi+PnjwIF9//TVJSUncv3+fevXq0aZNG+Li4mjbNjM5ZL9+/dizZ0+e7W7atImff/6ZjIwMoqKiOH/+PA0bNsxS5vjx40RERNC7d2/De2zRogW2trbo9XomTZpEly5dshzHpTx98uAuiL6DbtBoo77n3byWRszdDJp6WmKeR06r4mZqKmjV0Zr9QQ85sj+BDr62WFrlLybhWhExcBTyuwXIrWsQPV8tpmjLBtW5UvJFSom2ajHExaB7f26xJQAVOhNEv8Fo1Woiv/8SbdZEdGM+QNSoWyztKSXL2LQby5Ytw8XFhcDAQDRNo2bNmoWuX6/XG85YTE5OZsqUKWzdupXKlSszb948UlJSkFLm+APU1NQ0S+LRlP8/aeDq1at88803bNmyhXLlyjF+/HiSk59cSCylxMvLi//85z9PvLZlyxYOHDjAxo0bWblyJWvWrDHq/SrFS6b9/6hVzXrQqHme5dPTJWdCk7B3MKFajZI9MDgnFpY6Wne04Y9dmR2s9j42mJrl7xdj0aYTnA5BbvoN2cAdUat+8QRbBqhpQSVfZPA2CPkT0fdNRI06xd6ermVHdJM/BxMTtLkfoP2xq9jbVIqHtbU18fHxQOb5or///jsZGRlER0dz+PBh3N3dsbGxISEhwXDPgwcPcHV1RafTsW7dOqMXoLdq1cownRcREcHZs2ezLfeoY+To6EhCQgJbtmwBMs8ktbOz48iRIwBZFtlXrVqV8PBwNE3jxo0bhIaGApmnOlhaWmJnZ8fdu3ezjHTZ2NgY3nuLFi04evQokZGRQOb6sIsXL5KQkMDDhw/p0qULH3/8MWfOqE0dzwr5RyDE3DN6h+CFv5JJTpI09rBEFNH5gUXBrpwJLdpZ8zAug+N/JqDlM0WDEAIxcBQ4OKMt+wKZmJD3Tc8pNXKlGE1ej0SuXgGNWyB8e5dYu6JqDXRT56N9OzdzFOvaJcTLQxGm6uNbmjg6OtKyZUt8fHzo3LkzDRo0wM/PDyEEU6dOxdXVFQcHB0xMTPD19eWVV15h8ODBjBgxgs2bN9O+fXujM4cPHjyYcePG4evrS6NGjWjQoEG2x1/Y29vz+uuv4+vrS5UqVWjWrJnhtfnz5xsWtP/9fMGWLVtSrVo1unTpQr169WjSJPN0gUaNGtG4cWM6d+5MtWrVsiy8HzhwIIMGDcLV1ZW1a9eyYMEC/vnPf5KampnX7b333sPGxoa33nrLMHI2Y8aMgjxmpYjJtDTk1rVQuwH87bzUnCTEZ3DxbAqV3cxwdHn2vke5VjSjcXNLTh1PIvxEEo2b528Xo7CyRvf2JLS5k5G/LEUMf6cYoy291PE3OVDH32QlU5LRZr8DiQnopi9E2JXL8560NMmVCyk0bV6BxKT7hY8hIwO59ntk0Eao1wTdyPcQtvaFrrc0UcffGCcjI4O0tDQsLCy4fPkyAwYMYP/+/U+cK6qOv3mSOv4mK23PFuQv36CbOBPRoFme5Y8ciOdeVDo+L9oV+OgZKP5nGR6axKVzKTTysKRm3fyn0dE2/4bc+Ati2AR0bToXQ4RFq6SPv3n2utXKM0n+tgxuX0c34ROjOlZSk5w4lEDUzXQiz1/Do40Fzq6FS6AnTEwQA4ZlrsNatSRzHdY/pyCq1SpUvUrZk5SURP/+/UlLy9xp+umnnxp1YLui/J1MS0VuXQN1GkL9pnmWv3Mrjagb6TRoalGojlVJaNjMgsR4jfD/T9FQoXL+vj+LF/sjw0ORPy9F1mqAcKlQTJGWTs/2377yTNAOByMPBCK69zfqNzeAs6eTibqZTu0GevR6HX/uTeDCX8lFcn6grm1ndO/PASnRPnsf7XBwoetUSqe9e/fi5+eX5c+wYcOwsbExpEQICgoq0WzuStkh9+2A+zFGrbXSMjJTL1jb6KhRgJGgkiaEwKONFeUcTQj5M4H7MfkbwRU6E3TDJ4LQZa6/UiPAWahpwRyoacFM8s4ttJnjoUp1dJP8ESZ5J8K7fiWVE4cScatlTpMWlpSzd2LXtmvcup5GhcpmuLeywsy88Is85YNYtKWfwfkziK7/yFxkb0R8pVlhpgoSEhKwti6e3Z2llZoWfFJhPidlaVpQpqagTRkB5Stj8q5/nuUvnk3mTFgyrTpaFyhR5+NK6lkmJ2kcCHqIpkFHv/ynaNCOHkB+OxfR4xV0fQYVU5SFV9LTgmrkSsmRTE9D+/Zz0OnQDZ9kVMflfnQ6YUcTcXQxydwpIwRm5jpatLOikbsFUTfT2B/4kAf3C5+ETtg5ZK6D6PwicucGtEUfIxMeFrreskqn06mOhJKr9PR0dDr1YwFA7tsOcbHoer2eZ9nkJI2I8GRcK5oWSceqJFlY6mjV0YaMDMnhffGkpeVvvEXXsgOifRfk1jXIc6eLKcrSR625UnIkN/wIVy6gG/0Bwsklz/LJSRpH/0hAb6HDs501OpP/jU4JIahZzwJ7R1OOH0xgf9BDmrawomohc8AIUzPE66PQqtZE/rIUbfY7mQdBV6leqHrLIgsLC5KTk0lJSSnxM86eVXq93pCO4XknpUSn02Fh8fSyiT8rZEoKcts6qN8UUa9xnuXPnkwmQ4NGHpYlEF3Rsytngmc7aw7vS+D4wQRadbRGl48UEuLVEcjzf6GtmI9uxiKEtU3eN5VxqnOlZEueOobcGYDo/CKieds8y2dkSI4eSCAtTdKhiw16i+x/+3VyMcWrqy0hhxIJPZJIbHQ6jTwsMTEp3A97XceuyErV0L6egzbnPXRDxyFatC9UnWWNEAJLy9L5zb+4lKVpLKXoyOCt8OA+ulGT8ywbG53Otcup1K6vx8a29C5LcKlgRpMWlpw8lsTpkCSatDA+RYOwsEQ3/B20z95D+3EJupHvP/e/wJVY5+qf//wnFhYW6HQ6TExMmDNnDvHx8SxYsIC7d+/i4uLChAkTsLGxQUrJypUrOXHiBHq9njFjxhgyM+/du5f169cD0LdvX0P+mUuXLvHVV1+RmpqKh4cHQ4cORQiRYxtKzuT9aLTvFkKV6oj+b+VdXkpOHk3kfkwGnu2t8jyg1MJSRxtva86dSubC2ZT/v88aK+vCTUeIWvXRfTgfbekctKWfIV7sj+j9OkJXer/hKYpSsmRKMnL7emjQDFGnYe5lpeR0SBJ6C0GdhqV/xM+tlp7EeI0LZ1OwttVRq57x70nUqIPoPQi5/ofMDVAduxZjpM++Ep1cnzFjBp9//jlz5swBICAggCZNmrBo0SKaNGlCQEAAACdOnOD27dssWrSIESNGsHz5cgDi4+NZu3Yt/v7++Pv7s3btWkPW42XLljFy5EgWLVrE7du3DVmTc2pDyZ7UMtCWz4fUFHQj3kOY5T1td+lcCtevpFGvsQUVqxg3zafTCRo0s8SzvRUJ8Rns2/mQO7cKf0CzKOeI7p3ZiI5dkVvXoC2ZjUyML3S9iqI8H+TerfAwzqi1VtciU7kfk0HDZpb5PkrmWVW/qQUVq5hxJjSZW9dT83Wv6PYPqN8U+dsy5O3rxRRh6fBUVy4ePXoUb29vALy9vTl69CgAx44dw8vLCyEEdevWJSEhgdjYWEJDQ2natCk2NjbY2NjQtGlTQkNDiY2NJSkpibp16yKEwMvLy1BXTm0o2ZNb18K5U4jXRyEqVsmzfNTNNM6EJVOxqhl1GuZ/+3HFKuaZO1QsBYf3JXDudOHTNQgzM8Qb/0QMHA1nTqD5v4u8da1QdSqKUvbJ5KTMUatGHojaDXItm5Yq+etkMg5OJlR2K12L2HMjhMCj9f+naDiUyP1o4zfBCJ0O3VsTwMwcbdk8ZHrhf2EurUq0czV79mzef/99goKCAIiLi8PBwQEABwcHHjx4AEBMTAzOzs6G+5ycnIiJiSEmJgYnJyfDdUdHx2yvPyqfWxvKk+T5M8hNvyJaeyPa5Z0X6OGDDEIOJWBXzgT3VlYFnmO3sTWhva8tVaqbERGezOF9CaSmaHnfmAshBLpO3dFNnAWJ8Wj+k5ChhwtVp6IoZZvcsxXiHxg1ahURnkxqisz38TGlgYmpoFVHa/QWOo4cSCAxwfjvx8LBCd3gf8PVi8iAn4oxymdbia25mjlzJo6OjsTFxTFr1qxcc05kN3KR04dXCFEkiSkfJRsEmDNnTpbO3fNAe/iA6O8WYFK+Eo7jPkRnmXuem5TkDIK3X8fU1IRuvapgY5vzb26mpqZGPc/yL0oizjzg0L67HAhKpPMLFXApX8h1DM7eZNStz/3PPiD9q9lYvzoc6/5DEKV0u7mxz1IxjnqeRas0P08tKYF7Ozdg3rwtDq1y3wxzPyaVyAv3qdvQjtp1XYslnmfhWb7Qqxxb1l3n+MFkevStjLneyPWrfj15cOEMSTs2YNu2E/pmLfO+p4wpsc6Vo6MjkHlQasuWLblw4QL29vbExsbi4OBAbGwsdnZ2QObI09938ERHR+Pg4ICjo2OWk+JjYmJo2LAhTk5OREdHZyn/9/aya+Nxvr6++Pr6Gr5+nnYQSSnR/vMpxEaj+2AuMQlJkJCUY3lNkxzel8DDh+m062RDckocybnsZs/Pjiyn8tC+iw3H/khg6/rrNPKwxK2WeSF/MzRBTpiJ+Ok/JPy2nISzp9ENG4+wKH3n7KndbUVLPc+iVZqfp7blv8j4B6S/0C/X9yCl5FBwAqYmgup1iu9nxbPyLFu0s+RQcAI7Nl2jtZfxKRpkr4Fw8hj3F3ycmZ7BNvufvSWlTCYRTU5OJikpyfD/J0+epFq1anh6ehIcnHl0SXBwsOEUeU9PT/bt24eUkoiICKysrHBwcMDd3Z2wsDDi4+OJj48nLCwMd3d3HBwcsLS0JCIiAikl+/btw9PT01BXdm0o/yP3boXQQ4h+gxFutfMsfyYsmXtR6TRtYVksp76Xc8xM1+Dkasqp40mEHkkkPb2Q67DM9Yih4xEDhsPJI5nrsKJUFn5FUUAmJSJ3BkDTlogadXMte/tGGvei0qnX2CLHlDNliXN5M5p6WnIvKp1Tx5OMnikSej26tydBwgO0HxYVyQxTaVIiI1dxcXF88cUXQOZp9R06dMDd3Z1atWqxYMECdu/ejbOzMxMnTgTAw8ODkJAQxo4di7m5OWPGjAHAxsaGfv368cEHHwDw8ssvG9IqDB8+nP/85z+kpqbi7u6Oh4cHAH369Mm2DSWTvHoJ+d8V0MQT4dsrz/JXL6UQGZFCjTrmVKtZfOdnmet1tPay5vyZFM6dTiYu9iGe7a0LlUdGCIHw7YWs7Ib27dzMhKNvT0I0aVGEkSuKUtrIXZsgMR5dr9dyLZeRLgkPTcbWTodb7efnIPBqNfUkJmicP5OCtY2O2g2MW64hqtVE9BuMXL0CGbwN0enFYo702ZGvswXDwsK4fPkyycnJWa4PGDCgyAN72p6HswVlchLa7ImQnIRu+pcIW/tcy8fcTefg3nicXEzzNTxc2OHtO7fSCDmUiJQS91ZWRqd7yI28F4X2lT/cuIz4xxuIF/qVikWpz8pUQVmhnmfRKt9ttcMAACAASURBVI3PUybGo33wNtRphMm/Psy1bER4MudOJ9O2kzXO5Yt3h+Cz9iyllIQcSuTm1TRatLOiUlXjvg9LTUNb/AmcO41u6nxE5WrFHGn2ntlpwRUrVrB48WIuXbpEdHR0lj9K6SR//RaibqIb/k6eHavEhMyjbaysMs8JzM/RCIXlWtEMr6622NiacOyPRM6EJaFphZwmdC6PbvJchGcH5PpVyG8/R6Yk532joihligzaBIkJeY5aJSVqnP8rmYpVzIq9Y/UsEkLg3soKBycTThxOJPaecSkahE6Hbug4sLBEW/Y5Mi1/ubNKK6OnBf/44w/mzp371HcvKEVDO7QHeXAXoueriHpNci2bnp55tI2mSVp2tMHcvOTXGVhZ62jnY0P4iSQunk35P/bOOzyqMvvjn/fOTMrMJCGFJCSEEEIoSYAAAULoRXQVd+2ua1m7v9UFEcvqWrChLopiwYaKuq5lbbu6uq4GRAgIBEJogYTQpIWQAsnUzMx9f38MBGIqIW3C/TwPz0PufWfuyZs775x73nPOl6NlboaNNhEQ2HJbhL8/3HIPxCciP38fWbzfq0vYPboVLdfQ0OisSJsFmfUVDM1A9EpsdGx+njdvODnt7JWQ0ukEI8aayM6ysDbbyripZozmplM1RHAoyg13or70OPLz9xC/v6UdrO1Ymv3NFBQUhMnUeHm+hm8gDx9EfvA6JCUjpje+pSulJG+tjcqjHoZlmAgK7jgpGZ1OMDjdSNooIxXl3q7uZUea3+CuPoQQKOdegjLzESg/gjr3buS2ja1ksYaGRmdG/vAV2JuOWpWWuDi4z0XfAQFnLNPl6/gHePNhpcTbk7C6eT2wxKB0xJQLkUu+Rm5e18ZWdjzNvkumT5/OSy+9RGFhIYcPH671T8N3kC4X6pvPgl7v3Q7UNe4s7ch3cmifi4FDAoiK6Ryh8LjefoybGoReL/j5Rws7C1qhq3vqMJQH50NIKOoLc1B/+PdZV92ioXE2Ia1VyCVfwbBMRM+EBsepqlc/MNAo6Dug7Yp4fAlzsM4rXWZVWb/ShuppZgXhpX+E2HjUxS8iKyva2MqOpdnbgif0/XJzc+uc++STT1rPIo02RX7xPvyyE+WOBxFh3Rsde2h/NQVbHPSMN5DYv3MtKsHddIw7J4i8tTby8xxUlHpIG2k8I30vERmD8sA87wf/n2/DLzvh2jsQfp3rd9fQ0Dhz5Pf/BrsN5cLfNzpu785qqo6ppI8xotN3/qKX9iIi0sCQdCN5a21sWm9nyIimO9ULgx/KLfeizp2NuvhFlBmP+GxD56ZotnOlOVC+j9yYg8z6N2LydETaqEbHVh71sGGNjW5hOgaPaLm0TVti8BOkjzGys8DJtk0OKn+oYsQYE0EhZ9CuIcCIcttfkN9+ivzqQ+TBfd48rPDGHVENDQ3fQVoqkUu+RqSPRfTs3eA4p1OlYIuDiCg90bGdI3LfmYhL8MNm9VC41duiISm56RYNIrYX4oobkf94Hbn0a8TU37WDpe3PabuMpaWlFBYWdqoSUY2mkRVlqO8ugLgExGXXNzrW6VBZu8KCweBNXtTpOp9jdQIhBH0HBDB6ogm3S7LihyoO7D2zahShKCjTr0S540E4cgh17mxk4ZZWslhDQ6Ojkd//C6odiCaiVgWbHbhdktShXU8/sLXolxJAbC8D2zc7OPBL89ZeMeE3MGQk8vP3kL/samMLO4ZmO1cVFRXMmTOHGTNm8NxzzzFjxgzmzJlTI5Cs0XmRqgf1rfngcqHcei/C0HB/EtUjWbfKitMhGTHmzKrx2pOISG+7hpBQr5L75vXNzwNoCDFkJMoDz4HJjPr8w6g/fqPlYWlo+DiyqhK59D/eqFVMwz2XjlW42buzmt59/c4oGt7VEUIwZKSRsAgdeWtslDejRYMQAuWPM8EUjLroOaSzEf00H6XZ35yLFi0iPj6exYsX8+abb7J48WJ69+7NokWL2tI+jVZAfvMpFG5B/OH/ENE9Gx27ZYOd8iMehow00i283aQnW4WAQIXRk8z06efPnqJqVv1owW5rvpp7fYgePb0OVsow5IdvIN97GelytZLFGhoa7Y383xdQ7Ww0aiWlN4ndz1/QP/UMxePPAk60aAg0KuRkW7FaPE2+RgQFo9w4C4r3Iz99ux2sbF+a7VwVFBRw3XXXERDgvdECAgK45pprKCwsbDPjNM4cWbgF+fXHiIxJKJmTGx27p8jJ3p3V9B3oT89435R2UBRBytBAhmcaqTzmbddwpPjMnCFhNHkLAKZfiVyZhfrsA8ijWvNcDQ1fQ1YeRf74DWLEeESPuAbHHfzFRXmphwGDAjB0QF8/X8TPX2HkqS0anE0/2IrkNMS5FyN/+g65YXU7WNl+NPuuMZlM7N+/v9axgwcPYjQaW90ojdZBWipRF82H7tGIq29rdGzpYRdbcu1ExegZ0AWe1GLi/Bh/ThB+/oLVP1kpzD+zdg1CUVB+dzXKn+6Hg7+gPjkbuXN7K1qsoaHR1sj/fQkuF+LChvv7uV2S/I12QkJ19ErwzYfMjsIcpGPEWBN2q8q6ldZmpWaIi66BXomo772MrOg6D626Rx999NHmDDQYDCxcuBCLxUJxcTGrV6/m/fff59JLL6VPnz5tbGb7U1VV1dEmnBFSStRFz8GBPSh3PoqIiGpwrNXiYfVPVgJNCqPGm1u93NhoNGKz2Vr1PZuDn79CXG8/7FaV3TuqOVruIbKH/owS9EWPOMSQkcjcVV6x15BQRHzjnZ1bk46ay66KNp+tS2eeT1lZgXx7PmLEOJTx5zY4rmCrg5JDbtLHmDCaOi7XqjPPZWMYTQpGs8KuwmrsNpXoWEOjxQBC0SH6pyJ//Ba5uxCRMREhWj9aGBQU1Orv2RjNdq769OlDYmIiRUVF7Nq1C0VRuOKKK8jIyGhjEzsGn3eulv4HlnyNuOImlKEN/43cLsnqZRbcbsicaG6TBPaOXCQUnSC6pwE/f4U9O6s5+IuLsAjdmcnmBHdDZExC/lIEWV9B1VFITkMobb8Q++qC2xmQUkLJIeS6bNT/fo788HXc2zfhSR2O0PtWfmFnpTPfn/LfH8LO7Si3/QVhrv+L1lrlbUETG2+gT7+OjeB35rlsiuBuOoSA3YXVCCEIj2z88yXMwRDcDZb+B/wCEEnJrW5TeztXp7WipKamkpqa2la2aLQScu9O5GeLYchIxOTpDY+TktzVVixVKqMmmDAFdc2KGCEECUn+dAvVsW6VlZVLLAwaHkivPi1vDipMZpSZjyC//AD53efI/XtR/vQXRHBoK1qucabIygrktk2wbaNX1qj8iPdEWHfon4pz/SqoKEeZ+QgiUEtx6KrIo+XIZf9FjJqIiIppcNzWPDtCgYGDz179wNYiKdkfq8VDwRYHRpNCz96Nb7GKsecgt+Yi//0BcuBgRO+k9jG0jWjUufriiy+45JJLgMabiF55ZeP6dBrth3TYvPI25hCU62c2Go4t2OLg8EE3qcMC6X4WqLyHRugZPy2I3NU2NubYKS/1MGhYYIu3QYWiQ1z6R9S4BOR7L6E+MdvbcDTBtxcFX0Y67LBjKzJ/I3JbHhzY6z1hNMOAwYjfXIZIHgLdeyCEwFywiWMvzEF9/mGUWY8iTO37dKvRPsjvPgePu1Et1cOHXBw+6GbgkACfaUHTmRFCMCTdiN1mZWOOjUCTQnj3hl0OIQTKtX9G3V2Iuug5lIcXIAJ818lt1LkqKyur9/8anRf54RtwpBjlnie9odYGOPBLNTvynfTq40fvvmdP0qZ/gELGeBMFWx3syHdyrMJD+hgjpmYouzeEMnI8Mron6qtPoc67H3HtHU1WZmq0DtLthj2Fx52pjbC7ADwe0Bu8wuSjJiIGDoZeferdtg0YM5kqhwP19WdQn3sQ5a7HEcHdOuA30Wgr5NEy5E/fIUZPRkT2qHeM6pFs3WDHFKTQJ0mTu2otFJ0gPdNI9hILOdlWxk41Y25kh0SYzCg3zUZ97kHkx28irr+zHa1tXYTUuiLWy8GDBzvahNNGXbUUuXgB4sKrGlV5P1ruZuVSC91CdYyeaEZp4w7sERERnbKj/+GDLjas9uY0DM0wnrEwtayqRH3jb1CwGTHlQsRlN7R6Lk9nncv2QkoJB39BbstD5m+Ewq3gtIMQEN8XMXAIYuAQSBzQLE3IE/Mp8zegLpwLYZEodz+B6BbeDr9N16Mz3p/qR28if/ovyhOvIbpH1ztm53YH+RsdjBxvIqpH54jid8a5bClWi4fsLK/qx9ipZvz8G48Mqv/6APnNPxG33osyYlyr2BAT0/B2cFvQ7NjnDTfcUO/xm2++udWM0Wg5svgA8sPXoV8qYvoVDY5z2FVysq34+wvSx5ja3LHqzETFGBg/zUygSWHtCivbN9uR6hm0awgK9kY+pv4WueRr1AVzkFWVrWjx2YksP4K6Mgv1rfmo9/wR9dEZyE/ehpJDiNGTUP50P8oLH6B7cD7KJdd5HazTFNsWyUNR7nwUKspQ5z2ALCtpm19Go12R5aXI5d8hMqc06Fg57CoFWx1Exeg7jWPV1TCZdYwYY8Ju837/eJpo0SCm/x4S+iH//qrPfhab/Vjt8dTtuOp2u1HV5nfAVlWV+++/n7CwMO6//35KSkpYsGABFouFhIQEZsyYgV6vx+Vy8corr7Br1y6CgoKYNWsWkZGRAHz55ZcsXboURVG44YYbSEtLAyAvL4/FixejqipTpkzhoosuAmjwGl0J6XKhvjkPDAaUm2Y3WLXm8UjWrbTiqpaMmWLGP0DLKzCadYydamZLrp0d+U4qyjwMyzC2eG6EToe48mbUuD7Ivy9EnXs8D6tX12tX0lZIqwUKNiG3bfJu9R0+4D0RFIIYmAbJQxADhrS6mLbol4py9xOoC+agzrsfZfaTjSY/a3R+5H8/Awni/MsbHLNtkx2pQkqa7+b3+AJh3fWkjTKS+7ONjWttDM0wNpgTLPR6lFvuQX38TtS3nke5d267VGO3Jk16GY888ghCCFwuF3PmzKl1rqysjH79+jX7Yt9++y2xsbHY7XYAPvjgAy644ALGjBnDm2++ydKlS5k2bRpLly7FZDLx8ssvs3LlSv7xj39w1113sX//flatWsXzzz9PRUUFTzzxBC+++CIAb7/9Ng899BDh4eE88MADpKen07Nnzwav0ZWQn78L+3aj/PlhRFhE/WOkZPM6OxVlHoZnGgkJ7VoO5pmg0wmGjDASGq5jc66d5d9XkZ5pIjSi5XOkZE5GxsShvvo06t/uQ/xxJsrI8a1odddBuqqhaBvyREXf3p0gVfAP8EZiJ57ndapierW5eK5I6Idy91zUFx5BffYBlNlPNKo/p9F5keVHkNnfI8ZMbbDPX0Wpm/17XPQd6N9lq6U7E7G9/LBZVLZvdmAKctA/tWGHVnSPRlz9f8i3X0B++6k3muVDNPntMXmyNzG3qKiISZMm1RwXQhASEtLs1gxlZWXk5uZyySWX8J///AcpJVu3buXOO70JaxMnTuTTTz9l2rRprFu3jssv9z5pZGRk8M477yClJCcnh8zMTAwGA5GRkURHR1NUVARAdHQ0UVHeD1BmZiY5OTnExsY2eI2ugsxbg1zyNWLqbxFDRjQ4blehk317qumXEkBM3NmTwH469OrjT0iojnUrbaz80UJKWiC9+/q1+Atd9E5CeWg+6mt/Qy56DvWXXYhLrvW5J7DWRqoe2Lf7ZEVf0TZwVYNOBwn9EdOv9Fb09e7XIf2nRK8+KPc+hfr8I6jP/hXlrscQvdqvUaxG6yC//bTRqJWUks25dgICBUkDfV+VwlfoO9Afq0WlcKsTo0lHXCNd8JWMSahbcpFff4wcMATRd2A7WnpmNLlyTZw4EYCkpCRiY2NbfKF3332Xa665piZqVVVVhdFoRKfzftGEhYVRXl4OQHl5OeHh3oRSnU6H0WikqqqK8vJykpJOlrmf+poT40/8f8eOHY1e49dkZWWRlZUFwDPPPENERP0RoM6Ep/QwZe+9jL5Pf8JunY0w1H+T7t9rZdvGo8QnmsicEN3mT/+/Rq/X+8R8AkREQM84DyuWHGZLrg1blY7MiZEt1xeLiEA+9RpV77yI/bsvMBzeT8jsx1GCGq7kbAxfmssTSCnxFB+gemMO1ZtyqN68HmnxNunVxyfid97F+A1Ox5CShhJoalfbGpzPiAjcT79OxZwZyOcfodsjz2Pol9KutvkineX+9JQconRlFoHnXEhw//q/kAvzKzlWcYzx50QR3aPzteDoLHPZFkw6V/LD1wfZtM5Gj5hQomMbjmCpMx6k/O7rkYsXEPb8eygmczta2nKa/VgYGxvL0aNHKSoqoqqqqpZO24noVkOsX7+ekJAQ+vTpw9atW5u8Vn0FjEKIBrXhGhp/OkydOpWpU6fW/NzZqzSkx4M6/0FwuVBvvIuyY/UnTlsqPazIqiIoRCE5Td8hLTV8seplyEgDpqAAtm+xUHLYRvoYE0HBZxBxuvR6RGQM1f94nSN3X+8Vgo6NP+238ZW5bLh5ZwRiyChv0vmAwciQUJyAE8Bq9/5rRxqdT0MA3D0XOf8hyh+ZiTLjYUR/rYlyY3SW+1P94A0AnJMurNceV7VKzqoqQiN0BIc6KC11treJTdJZ5rKtGDLSQPYSJ1nfHmTsFDPmRtZXecMs1Hn3U/rSk4ib725RgKC9qwWb7VytXbuWl19+mR49erBv3z7i4uLYt28fAwYMaNK5KigoYN26dWzYsIHq6mrsdjvvvvsuNpsNj8eDTqejvLycsLAwwBt5KisrIzw8HI/Hg81mw2w21xw/wamv+XVPrtDQUIKCghq8hq8j//MJ7MhH3DS7waRbV7XK2mwriiIYMdaMvpU1A7syQgiSkgPoFqYjd7WNFT9UkTbSeEZbqsq4aciYXqivPYP69L0oN85CDMtsRas7jlrNO7dvhP17vCdqmnde6s2biuzR7pHTM0GER6Lc97R3i/ClR1FufxCRMrSjzdJoBHmkGLlqCWL8eQ3moBZsdVLtlGRMCPSp+7ErYfBTGDXOxIosC2uWe3tgNVRIJBIHIC78vVfCKHU4YvSkesd1Jpq91/HJJ59w++23M2/ePAICApg3bx633norCQkJTb72D3/4A6+//joLFy5k1qxZpKamMnPmTFJSUli9ejUAy5YtIz09HYDhw4ezbNkyAFavXk1KSgpCCNLT01m1ahUul4uSkhIOHTpE3759SUxM5NChQ5SUlOB2u1m1ahXp6ekIIRq8hi8jCzYjv/kEkTkFJWNi/WNUyfqfbdis6nEBUq0ysCV0jzYwfloQwSE61q+ysXWDHfVM2jUkDkB5aD6ccLL+9QHyNCpuOwvS7UYW5aN+/TGeefejzvoD6kuPI5d9663qu+Q6lAfno7zwd3R/uh9l4vmIqBif/CIT3cJR7n0KImNRX3kCmbemo03SaAT57acgFMT5l9V7vuqYhz07nMQn+mmFPR2M0axj5FgTDkfTLRrE+ZdDUjLyw9eRJYfa0cqW0ew7q7S0lNGjR9c6NmHCBG699Vauu+66Fl386quvZsGCBXz88cckJCTURMAmT57MK6+8wowZMzCbzcyaNQuAuLg4Ro8ezezZs1EUhZtuuglF8ToNN954I3PnzkVVVSZNmkRcXFyj1/BVZFUl6lvzISoGcdWtDY7L3+jgSLGbwemBjUoOaDRNoFEhc5KZ/I12dhU6qSh3k55parFEhvfL+mnkP15DfvNP5L7d3hYaxvbNNzodTjbvPL7NV7DlZPPOXomIaRd5I1PNbN7pa4igEJR75qK++Cjq688gbprdas0NNVoPWXLIG7WadEG9jWCllGzZYEevF/QfpCWxdwZCI/QMHWVk/SobeWtsDBtdf4sGoehQbrob9bGZqG/NR7nvmU4tuN7sDu0zZszgiSeeoFu3btx3333cfPPNBAUF8eCDD/LOO++0tZ3tTmfs0C6lRH35CdiWh/LAcw32Ttq320neWjsJSX6kDut4MdqulDtwYG81G3Ns6PSC4ZlGIiJb3nRQSolc9l/kJ4uge7R3y6lHz0Zf055zKcuPHM+bykNu3wTHKrwnImMQycc7ofcf5HN6fKoqqSjzUFbiJi4+lEBz8/O8pN2G+vLjULQdcf0MlMwpbWip79HRn3V18YvInBUoT72J6FY3BeTQ/mrWrbSROiyQhE4uc9PRc9neFG1zsG2Tg6RkfwYMaiTBPScb+eY8xPlXoFx8TbPfv9PmXE2ZMoXt27eTkZHBBRdcwGOPPYYQgunTp7elfRqnIJd8BZvXIa66tUHHqrzUzaZ1diKi9CRrTfFandh4P4K76chZaeXnZVYGDgogcYB/i7a7hBCISecjY3uhvv431KfuRrn5bsSQkW1gedN4m3duPhmd+nXzzoGDvYno4ZEdYl9LkVJitagcKXZzpNhFaYkbj9t7rmDLIfql+NMvJaBZf0MRaES58zHUV+ciF7+IWu1EmXh+2/4CGs1ClhxErv4RMfnCeh0rj1uyNc9BUIhCfKLWjqazkTjA26JhR74To0mhV5/6nV9lxFjUrbnI/36KTE7rtEUmLdYWLC0txeFw0LNn40/avkpni1zJvUWoT98Hg4Z7O37X80Vgt6ms+KEKnV4wrhn6Te1FV3wCc7skeTk2Du1zER1rIG2kEYNfy/OJZPkR1Fefhr1FiN/9AXH+FQil7t+vNeeyyeadJ3T6YuN9Lleq2qlSetjNkcNeh8pu8y5zRpNC92g93aP1hEXo2VUgKdpeRWwvA0NGGtE1Uw5KuqpR35gHG9ciLr8RZdpFbfnr+Awd+VlX33kBuX4lytOLEMGhdc4XbnVQsMXB6IkmIqI6v8xNV1w3m0JVJWuWWykrcZMxoeG/k3TYUZ+4C1zVKHNebFb0vL0jV5pwcwN0JudK2m2oT8wCjxvlkfpvJLdbsmqpBWuVh7FTgwgK6TyNKrvqIiGlZHehk/yNDowmhfQxJoK7tXzeZbUT+feFyNXLYNholBvuRATU3tY9k7ms1bxz+0bYke9t3qko0Kf/cWcqDRKSEPrO/+VzKqpHUl7m8UamDrs5Wu6V69IbICLSUONQmcy1/z7h4eGszj7A9k0OQsN1jBhrarb0kXS7kW/NR65fifjd1YgLrvA5J7S16ajPuiw+gPrIHYhzfoty+Y11ztusKj/+t5KoGAPpmZ03t/FUuuq62RSuasnKJVXY7ar3u6yBFg1yzw7UZ+6DtFEot/2lyc9ep9oW/NOf/tSsN3nttddaxRiNukgpkR+8BqUlKPc+Va9jJaVkY46NYxUeRo4zdSrHqisjhKBP/wBCwvSsX2VlRVYVg9ONxPVu2ZaD8POHG++C+ETkp4tRn74P5Y6/IiJbtihIKeHIoZPO1PbNYPU27yQ2HjHhPG9kql9KHSeusyOlxFJ1cquv7Ih3q08I6Bauo19KAN2j9XQL06EoDS+6Qni7c5vMChvWeFtujBxnbpaTLPR6uOUe8PND/vsfUO2Ei6896x2sjkB+8wkYDIhzL6n3fP5Gb15d8hAtVaKzY/ATjBxvJjurijXLrYxroEWD6J2EuOga5OfvIbN/QIzrXMorjTpXM2bMaC87NBpArlqKXPuT98k4KbneMUXbnBz8xcXAwQFExfhWxKErEN5dz4Rzg1j/s7fapaLUTcrQwGZvMZ2KEAIx9XfI2N6ob8xDnXs3yi33IlKHNev1svKod4tv+3HR4xOK8mERiLSRMDANMWAwIqTutklnx3liq6/YzZHDLhzHt/pMZoW43n5EROmJiDS0aHs2Js4Po0lh7QorK5dUMTzTRGSPpj9LQqeD6+8EP3+vSHC1E668WXOw2hF5aD9yzXJvxWpwtzrnSw+7OLTPRf/UAK0ljY9gNCmMHGti5Y8W1q6wkjnJjK6ePo1i2sXIrRuQHy9CJiUjojtPmpK2LdgAnWFbUB7aj/rkXZDQD2X24/Vq0hUfcJGTbSW2l6FRlfGO5GwJb6uqpGCzg6LtTkJCdWfcX0weKUZ99Sk48ItXk/DcS+jevXutufQ278xHbsvzOlM1zTtN3uadA47nTflgjymPR1JRdtyZKnZzrMK71WcwCCKivNt83aP0GM0tj9T++t6021TWrrBQeUwldWjzK8qklMh/voPM+jdi3DTENX86KzUkO+Kzri56DrlxLcrTbyF+JSulqpLl31fhdsOk84Lq/YLurJwt62ZjnKju7NHTwPDM+r/fZEUZ6uMzIaw7yv3PIgz1PxR1qm3BU/nkk08aPHfllVe2ijEaJ5GuatQ354GfP8rNs+tdqCuPeshdbaVbmI4hIzqnY3U2oSiCgUMC6RauI2+tjeXfVzEsw9isCEh9iO7RKPfPQ777EvLz9+CXXah3PoysSULPg10F4PF4k4uSkhEXX+vNm4rv43Nf7lJKLJVqTRJ6WYkbj8e71RcarqN/qnerLyS08a2+MyHQqDBmchC5q61sybVjqfSQMjSwyesJIeCKG8HfH/nNP70RrBtmeSNbGm2GPPgLMmcF4rxL6jhWAHt3VlN1TCV9jNGnHCsNLz16+pE8RCV/o4PtmxwMrGdbV4SGo/xxBurCp5D/+gBx+Q0dYGldmu1c/VqT7ujRo+Tn5zNyZMeUjXd15KfvwP49KDMfqbcZntPp7Wir1wvSx5i0haMT0aOnH8EhOtattLJmuZV+KQH0S2lhuwb/ALj1XuiViPzyfY5cey5IebJ55zkXeSNTfQf6ZPNOp6N2VZ/DfspWX4If3aMNhEfqMRja7/7WGwQjxpjI3+hgV6ETq0VleKapSRuEEIiLrkH180d++Xeky4Vyy90+VxzgS8j/fAJ+AYhpF9c553SqFGx2EBGlJzpW+xv4Kn36e1s0FG13YjQrxCfWXedEWgZiwnnI779EpqQhkjteoqrZztXtt99e51heXh7Z2dmtapAGyA2rkT9+izjnZsPXKQAAIABJREFUd4hBdeV6VFWyfpUNh10lc7KZQKOWR9DZMAXpGDM1iM3rbRRudVBR5mZYhrFF7TGEEIjfXIqMTyRgZz6OmN4wwPead8Lxrb7SE3lTp2z1+QkiIvU1VX1GU8dGfIQiSBkaiDlYYfN6Oyuzqhg53tQsu5TzL/c6WJ+8hfpqNcqf7kcYtL5KrY08sBe5Lhvxm8sQ5rpRq4LNDtxuSepQTT/QlxFCkDosEJtVZfN6+/F2KnWdZXH5TcjCrajvvOhtzxAU0gHWnuSMvpUHDx5MTk5Oa9miAciyI6jvvgTxfRGX1C8rtHWDnbISN4NHGAkN77zt/8929HpB2kgjg9MDKStxs/z7Ko6WuVv8fiI5jaAbZiKGZ/qMYyWlpOqYh50FDlb/ZOG7L4/x8zIrOwuc6PTQPzWAsVPNnPu7YNLHmIhP9O9wx+pU4hP9GTXBhN2usuIHC+Wlzfv7KVN/i7j2dtiyHvXlJ5BORxtbevahfv0R+Acg6ukxdqzCzd6d1fRO8teqp7sAiiIYnmnCHKywbpWVyqOeOmOEvz/KLfeAtRL1vZfp6HTyZn8zHz58uNbPTqeT7OxsIiLqVx3XOH2kx4P61nOgelBuvafe7YQ9RU72FFWTOMC/xSX/Gu2HEIL4RH9CQr3bhCuXWkgZGkh8ol+XfZp2Ok7mTZUedp/c6gtSiO/jR0SUgYhIPfp23Oo7E7pHGRg7NYi1K6z8/KOFtJFGYuOb/uwp48/zRrDeeRF1wRyUGY90av1IX0Lu3w3rVyGmX1nnQUNKyeZcO37+gv4pvrdVrlE/BoNg1HgzK36oYu0KC2OnBtXRdxVxCYhLr0d+8hZy2X8RkzpOPaHZztXMmTNr/ezn50dCQgJ33HFHqxt1tiK//giKtiFuvrve3kalJW625NqJ7KFnoCY66lN0C9MzfloQuattbF5vp6LMzaDhRvRdIFfO45GUl56s6jvxVGnwE3Q/XtUXEWXw6TL4oGAdY6eaWbfSSu5qG5YqtVl5dErGJKTBD3XRc6jPP4xy12M+E3XszKhffwyBJsTU39U5d2Cvi4pSD0NGBGLw8917TqMugUaFkeNMrFpqISfbyuhJ5jprqJhyIXJrLvLTd5D9UhCx8R1ia6tUC2qcOXLbRuS3nyLGTEUZNaHOeZvFw7qVVkxmhWEZJkQbVUtptB1+/gqjxpvYke+kYIuDYxVVpI8xYQ7yrW0L71afypHDLo4Uuyk74kb1gFAgLFzHgEHHq/q66brUfervr5Axwcymdd48OmuVp1mSOWL4GBSDH+prz6A+9yDKXY/X249Jo3nIX3ZB7s+IC3+PMJlrnXO7JPkb7YSE6ohL0CL7XZFuYXqGjTaRk21lw2ob6WNqV8oLIVBuuBP10Zmoi55DeXB+h+Q86h599NFHmztYVVUKCgrYsWMHNpuNsLCwLru1UVVV1W7XkpVHURfMgW7h3o7c+to+r9sl+fknC24XjJ7kewnsRqMRm83W0WZ0CoQQhEfqCQ3XsW+Pi707nZiDlAYlHn5NR82lw65SfMDFzgIHm9fb2Vng5EixG0VATJyBfikBDB5uJL6vP+Hd9QQEKj6xNpzufCqKIDrWgKIIdu+opvSwm6gYQ5MRSBEVi+jTH7nsW+SGnxFDRyMCfasrfnNoj/tT/eBVqDyKcuu9db40C7Y6OFLsZsQYE4GdKHevJWjrZsOYg3UY/AS7C5143NRJcBf+gYiYXsisr8BhR6QOJyiofSPGzY5c7d27l2effRaXy0VYWBjl5eUYDAbuueceevfu3YYmdm2kqqIufhGsFpRZj3pL7089LyUb1tioqlTJGO97UQ6N+onsYWD8tCDWr7KybqWNxAEeBgwKaLP+TaeLx33KVt9hF5VHVaD2Vl/3aIPPOfqtgRCCpOQATEHHJXOyLIxqhuyUSE5DmfUY6kuPoT77AMrsJxARUe1kdddA7t0JeWu84ubG2lErS5WHXQVOevY2EBqhFfp0dRKS/LBWedhZ4G3R0Ltv7fw6MSjdu0W45GtkchrE1N1CbkuafQe+9tprnHvuuUyfPh0hBFJKvvnmG1577TX+9re/taWNXRqZ9W/Ysh5x9f8heibUOV+wxUHxARcpQwPrLT/V8F2MJoXMyWa2brCzc7uTo2Vuho021UnSbA9qtvqKXZQUuykvPWWrL0LPgEF+NQ08fSEi1R7ExPlhNCqszbaSvaSK4aOblswRSckos59EXTAHdd4DKHc/iYhq387Rvoz69UdgNCOm/LbOufw8O4oCAwdr+oFnA0J426XYrCpbcr0tGn79+ROX/hFZsNlbgX9e+zpXzV7FDx06xAUXXFCzsAohOP/88ykuLm4z47o6cvcO5Bfvw7DRiAm/qXP+4C/V7Mh3EpfgR0KSlj/QFdHpBIPTjaSNMlJR7mH591WUHWl5u4bTwWFX2benmtzVVn74qpKf/ldF/kYHDrtKfKI/I8eZOO/iEDInmUlKDqBbmF5zrH5Ft3A9484JwmhSWLPCyu4dziZfIxKSUO6ZC24X6rMPIA/80g6W+j5yzw7YuNarIfirLdXDB10cPuimX0pAhzycaHQMiiIYPtobNV5fT4sGYfDztmdw2NvdtmbnXO3duxedTkdsbGzNsZycHNxuNxkZGW1lX4fR1jlX0mZFXfAI+AWgzJxTp7v20XI3a7OtdAvXMTzT1Gm2i1qCljvQNCHddETHGCg+4GJ3obcHVGh43SjRmcyl2y0pPexmz85q8vPsbNvkjYo6HZLuUXoS+/uTOsxIUnIAkT0MmIPaTmams9Aa96bBIOgZ70fVMQ+7C6txVatERDXuiIqQUMTgEcjVPyKzv0cMTEN0CzsjOzoDbflZV99fCNaqOm1qVI8kJ9uKn79g6Ehjlymi0NbN5qHoBFExBvbvrebA3mpie/nVavMigkIguBvBqWntaleztwVVVWXBggX06dOH8PBwysrK2LVrF+np6bzyyis14/785z/XeW11dTVz5szB7Xbj8XjIyMjgiiuuoKSkhAULFmCxWEhISGDGjBno9XpcLhevvPIKu3btIigoiFmzZhEZGQnAl19+ydKlS1EUhRtuuIG0NO+E5eXlsXjxYlRVZcqUKVx0kbexXEPX6EiklMgPXoWyEpR7n6pT8eJ0qDWLxYgxpiarkTS6BsHddIw7J4i8tTby8xxUlHpIG2lscT8oKSWVRz3He065KT/iRlVBOb7V13Owd6svuJu21Xem1CeZM2x045I5okccyr1Poz7/MOr8h1DunINIHNCOVvsOclcBbF6HuOQ6REDtqNWuHd75HjXehKKtlWclJ1o0rFxqYe0KK5mTa7doUMZNa3ebmh252r9/PwMHDiQ6Oprg4GCio6MZOHAgISEhmM3mmn8pKSl1XqsoCmPHjuX8889nypQpfPTRR8TFxfHZZ58xadIkbrvtNjZv3kxFRQWJiYlkZWVhs9l4+OGHCQgI4LvvvmP06NHs37+fzz77jHnz5jFixAgWLFjAeeedh5SSp556igcffJCLL76YxYsXk5ycTHBwMG+88Ua912iKtoxcyZVZ3rYLv7saJWNirXMej2TNcis2q8roiWZMXSCBXXsCaz46nSAmzlt9tqeomoP7XURE6vEP8G51NDWXDrvKof0nq/p2FXor2nQ6iO3lR7+UAAYNNxKf6E+YD1X1tRWteW8KIYjsYcA/QLBnRzWHD7qI7GHA4NeIg2UKQgwdjcxd6W16mDjAp5Pc2+qzrr7/CtisKDfX1mp02FXWrbISGa2nX0rXyrXS1s3TIyBQIbibjl2FTqqOeoiJM9Ra2zptteDll1/e4osIIQgI8FbBeTwePB4PQgi2bt3KnXfeCcDEiRP59NNPmTZtGuvWrau5XkZGBu+88w5SSnJycsjMzMRgMBAZGUl0dDRFRUUAREdHExXlXZQyMzPJyckhNja2wWt0FPLQPuRHb8DAIYjfXFr7nJTHG0x6GJ5pJCRUq3g5GxFCkDgggJAwPbk/W1nxQxVDRtTfFdztlpQfOVnVV3XMW9Xn53+iqs9ARJT+rKzq6yh69/XHZPbKdKz4oYqRY02NVq+J8O4nI1gvPoZy+18RqcPa0eLOjdy5HbbkIi79IyKgtgO1baMdqUJKWtdyrDRaRlSMgdShgWzJtbN1o4PUoR13X5zWt/eWLVtYvnw5FRUVhIaGMn78eFJTU5v1WlVV+ctf/kJxcTHnnnsuUVFRGI1GdDpvZOZEeweA8vJywsPDAdDpdBiNRqqqqigvLycpKanmPU99zYnxJ/6/Y8cOqqqqGrzGr8nKyiIrKwuAZ555pk1kfaTTSfmTzyMDjITf8wS6sNrX2LrxKPt2H2NIeiiDh4Y38C6+h16v12SSWkBEBPSKd7Psf8XkrrZhtxqIGK8gZBAH9tk4+IuNw4fsqKo34hXVI4D+yUZi4oyERXRdeZ3WpK3uzYgI6BFTTdY3B/l5mZWxUyLpk9TIk3NEBOrTr1Px2CzcC+cScs8TBIwa3+p2tTVtMZ8VCz/DFdyN7pddV8u5OnzIzv69Rxk8PJT4hK6zXp5AWzdbRkQEqJ4j5G88RmRUEMmDO6Zhb7OdqyVLlvDRRx8xefJkkpKSKC0t5cUXX+TKK69k6tSpTb5eURSeffZZrFYrzz33HAcOHGhwbH2CiyfaP5zO+NNh6tSptX6P0tLS03p9c1D/8Rpy706UO+dQoQKnXKOk2MXalVaiYw3E9VHb5PodRURERJf6fdqb9LH+bNsk2bb5GIX5lXg83vs9KEShd5I/3aP0hHXXH88x8CCpoqysY232Fdr63hw92ci6bCs/fX+Y4oPHSEpuXDJH3vkYvPgox+b9lcqbZqOM9C0Hq7XnUxblo+atRVx+A2UWK1is3uOqZOWPFgICBbHxXWu9PIG2bracPv2g7IieNdmlSOxExRiIiWnflifNdq6++uorHnrooVoNQzMzM5k/f36znKsTmEwmkpOTa7q8ezwedDod5eXlhIV5q2VOJMyHh4fj8Xiw2WyYzeaa4yc49TWnHi8rKyM0NJSgoKAGr9HeyPWrvDkV516MSB1e65ylykPuKhtBwQpDRxm1iINGLRRFkJIWSFiEjsoKPcYgN92j9FrJuQ/g76+QMdHMphwbBVscWKo8DBnRsGSOMJlRZj+O+vITyLfmo7qqUcY0f33taqhffQTB3RATagvw/rK7mmMVHoZltLzgQ6PrIhTBsAwTq360sP5nK2Mmm2ln36r5fa6qqqro2bNnrWMxMTFYLJYmX1tZWYnV6n3iqK6uZvPmzcTGxpKSksLq1asBWLZsGenp6QAMHz6cZcuWAbB69WpSUlIQQpCens6qVatwuVyUlJRw6NAh+vbtS2JiIocOHaKkpAS3282qVatIT0/3Nhlr4BrtiSw9jPrey5DQD3HRNbXOuaola1dYEQqMHGfSFgqNBunR048xkyKJ6+2nOVY+hE4nSBtlpP+gAA7sdfHzjxacDrXB8SLAiDLzURiYhnz3JdQfv20/YzsRsnArbNuIOO9ShP/JVjWuapXtmx2EReiI6aU1VtaoH71BMHKct2J37Qpr+1+/uQP79+/Pe++9xzXXXIO/vz8Oh4MPP/yQfv36NfnaiooKFi5ciKqqSCkZPXo0w4cPp2fPnixYsICPP/6YhIQEJk+eDMDkyZN55ZVXmDFjBmazmVmzZgEQFxfH6NGjmT17NoqicNNNN6Eo3i+ZG2+8kblz56KqKpMmTSIuLg6Aq6++ut5rtBfS7UZ9az4gUW6p3Z9FqpLc1VZsFpWMiWaMPq6FpaGhUT9CCPolB2A+LpmTnWVhZCOSOcLfH+XPD6G+8Tfkh6+jVjtRzr24na3uWNSvPoSQUMSE82odL9jioLpakjosUIvyazRKQKDCqPFmspe0n1bwCYRsKJHpV1RUVPDiiy9SUFCA2WzGYrHQr18/7rzzzg7bamtLDh482Crvo37xPvK/nyFuvRdlxLha5/I3emVPBg0PrKOL1JXQcgdaD20uW5eOmM+KMjc52VY8HsnwTBORjchaSbcb+c4LyJwViN/+ATH9yk7tULTWfMqCzajPPYj4/S0oUy6sOV51zMNP/6uiVx8/Bqd3PeHrU9E+661HySEXacPj2/WaTUaunE4nn3/+Ofv27SM5OZkZM2bUVAueWqGnUReZvwH53eeIcdPqOFb7dlezc7uT3n39urRjpaGhUZvQcD1jpwaRs8LC2uVWUoc1/HAl9Hq4eTYY/JBffQjVTrjkuk7tYJ0pUkpv1KpbGGL8ubWOb9lgR28Q9B8U0Mg7aGjUpinNz7agSefq7bffZufOnQwdOpQ1a9ZgsVi48cYb28M2n0ZWVqC+/QJE90RceUutcxWlbjatsxEeqSelA/twaGhodAxGk8KYKUGs/9nK5vV2LJUeUtIC65VuEYoO/jgD/PyQ333udbCuvBmhdNG8u+2boHAr4qpbEYaTvd0O7XdRethN6rBA/P276O+u0WVo8g7Ny8vjoYce4pprruGBBx5g/fr17WGXTyNVFfWdBWC3odx6b61kTLtNJWellYBAhfRMY5fXbtPQ0KgfvUEwcqyJPv382b2jmrXZVlyu+rM0hKIg/vB/iGkXIZf+B/n3hUjVU+9YX8YbtfoIuoUjTpEscbsl+Xl2gkIU4hM1EXuNzk+TzpXT6SQ0NBTw7gFr7fibRv7wL9i6AXHlzYievWuOe9xegVG3WzJynAk/7elLQ+OsRiiClKGBDBoeyJFiNyuXVGGz1l9JKIRAXHYDYvqVyOwfkG8vQHq6mIO1bSMU5SPOv7xW1Grndid2myR1mPZAquEbNLkt6PF42LJlS83PqqrW+hlodpf2swG5qwD55d9heGadfIGNOTaOVXgYMbbhKiENDY2zj1Mlc7Kzqhgx1kRoeN3lWQiB+N3VqH7+yC/eR7qr61Qh+yo1uVZhEYix59Qct1lVirY7iIkzEBGpSYJp+AZN3qkhISG89tprNT+bzeZaPwsheOWVV9rGOh9D2iyobz4L3cJRrvtzraTTou1ODvziYsCgAKJjfX8h1NDQaF26RxsYOyWItSusrPrRwtCRRmJ61b8FpvzmMq+D9fEi1IVPofzpfoSfjxfGbN0AO7cjrrkdYTi5Rubn2QFI1vQDNXyIJp2rhQsXtocdPo+UEvn+QqgoRbnvGYTRXHOu+ICL7ZscxPQy0Hegjy+AGhoabUZQiI6xU83krLSy/mcbliq1QckcZcqFXgfr7wtRX3oc5c8P1RE29hVqolbhkYgxU2qOlx52cWi/i/6pAZr4uIZPod2trYRc8T1y/UrERdciEgfUHK865iF3tZWQUB1DRmjSNhoaGo3jH6AweqKZ2HgDBVscbFhjq9GT/DXKuGmIG++CHVtRF8xB2tq/E3WrsCUXdhd6c62Ob3GqqmRLrh2jSSFxgPZQquFbaM5VKyAP/IL8eBEkpyFO6aJc7VRZu8KKXi8YMdZ0XFhXQ0NDo3F0OsHQUUb6px6XzFnWsGSOkjER5db7YE8R6vMPIy2V7WztmSGlRP33PyAiCpF5Mmq1t6iaqkqV5LSABrUYNTQ6K5pzdYZIpxP1zXkQEIhy4101vWdUVbJ+lQ2HXWXEGJMW0tbQ0DgthBD0Swlg+Ggjxyo8ZGdZqDpWf3WgGJ6Jcsdf4cBe1OceRFZWtLO1Z8CmdbC3CHHBFd6mqYDToVKwxUFElF7LUdXwSbRv/DNE/vMtOPgLyk2zESGhNce3brBTWuJmcLqR0AitwkVDQ6NlxPTyI3OSGY9Hkr2kiiPFrnrHiUHpKDMfgSPFqPP+iizv/NIpUkrUrz+C7tGIjEk1x7dvduB2a/qBGr6L5lydAWpONnL5/7yq7SlDa47v3elkT1E1ffr7E5egNbzT0NA4M05I5gQaFdYst7KnyFnvODFwCMpdj8GxctRnH0AeKW5nS0+TjWuPR62urIlaHS1388uuahKS/AkK1lrWaPgmmnPVQuSRYuTfX4GEfojfXV1zvKzEzeb1drpH60kerOlfaWhotA4nJHO6R+vZvN7Olg12pFo30V30TUaZ/STYrKjP/hVZfKADrG2amgrByB6IjIk1x7bk2vHz926Jamj4Kppz1QKk24266DlAHG/g533isllV1q2yYjQrDB9trFcnTENDQ6OlGI5L5iQk+bG70MnabCvueiRzREISyr1zwe3yRrAO7O0Aa5tgw2rYtxsx/fcInTdCdWCvi4oyDwMHB2Dw09ZPDd9Fc65agPz3P2B3Icp1dyC6RwPgdklyVlhQVa+0jcFPm1oNDY3WRyiC1GHGGsmc7AYkc0TPBJR7nwZF8Uaw9hZ1gLX1I1XVm2sVFYsYOR7wrqH5G+10C9Np6RQaPo/mAZwmcusG5HefI8afh0gf6z0mJRvW2qisVBk+2oQ5SMsT0NDQaFt69/Vn5HgTdptKdlYVFWXuOmNEj54o9z0DAYGo8x9CFm3rAEvrYcNq2L8HMf3KmqjVjnwHToeWxK7RNdCcq9NAHqtAfft5iI1HXHlTzfHCrQ6K97tIHhJAZA+tbFhDQ6N9iDwumaPTCVb9aOHgvuo6Y0T3aJT7noagbt5Go9s3dYClJ6mJWkX3RIwcB4ClysPOQidxvf3q1VTU0PA12uUuLi0tZeHChRw9ehQhBFOnTuX888/HYrHwwgsvcOTIEbp3785dd92F2WxGSsnixYvZsGED/v7+3H777fTp0weAZcuW8cUXXwBwySWXMHHiRAB27drFwoULqa6uZujQodxwww0IIRq8xukiVdXrWDntKLfMrdHxOrivmsKt3kWhTz+ti7CGhkb7UiOZk21l/SoblkEqSQNrS+aIsO4o9z2N+vzDXqmc2x9ApA7vEHvl+lVwYC/ilnsQijdqtXWDHZ0CA7QiII0uQrtErnQ6Hddeey0vvPACc+fO5X//+x/79+/nX//6F4MGDeKll15i0KBB/Otf/wJgw4YNFBcX89JLL3Hrrbfy1ltvAWCxWPjss8946qmneOqpp/jss8+wWCwALFq0iNtuu42XXnqJ4uJi8vLyABq8xuki//cFbNuI+P2tiNheAByrcJO3xkZouI5B6VooW0NDo2PwD1AYPem4ZM5mB3n1SOaIkFCUe56CHj1RX5mLzP253e2Uqgf59UfQIw6RPgaAwwddlBxy0y8lgIBAbTNFo2vQLndyaGhoTeQpMDCQ2NhYysvLycnJYcKECQBMmDCBnJwcANatW8f48eO9HYr79cNqtVJRUUFeXh6DBw/GbDZjNpsZPHgweXl5VFRUYLfb6devH0IIxo8fX/NeDV3jdJA7tyP/9QEifSxi7DmAt4NwTrYVg58gfYxJk2fQ0NDoUE6VzNm/18XqZRacztqJ7iIoGOXuJyE+EfWNv6Gu+aldbZTrVsKhfYgLr0IoOjweydYNdkxBCglJWuRfo+vQ7o8JJSUl7N69m759+3Ls2DFCQ71dzUNDQ6ms9GpilZeXExERUfOa8PBwysvLKS8vJzw8vOZ4WFhYvcdPjAcavEZzkVaLt+1CaATi2jsQQqB6JDkrrTidkhFjTdrTloaGRqfghGTOsNFGjpZ7yP7BQlVlbckcYTR7G40mpSDffh41+4d2sc0btfrYm7M6PBOA3YVOrBaV1KGBKNoDqkYXol0zBx0OB/Pnz+f666/HaDQ2OE7Kevq2NLDlJoSod/zpkpWVRVZWFgDPPPMMERERSCk59s7zOI+WEfbU6xh6xSOlZOWPR6go9TBhWhR9koLO+NpdHb1eX8tZ1mg52ly2Ll11PiMioEeMgyXfHmLVEiuTzosmJq72misfe5Gjf3uA6vdexuhnwHj+ZWd83cbm0778eyqL9xNy31wCIiOxWd3syN9LrwQTyYN6nPG1uxpd9d48W2g358rtdjN//nzGjRvHqFGjAAgJCaGiooLQ0FAqKioIDg4GvJGn0tKTulhlZWWEhoYSFhZGfn5+zfHy8nKSk5MJDw+nrKys1viwsLBGr/Frpk6dytSpU2t+Li0tRV32X+TPyxCXXc+xsCgoLWV3oZMd2+wkJfsTHOqktLR+GQqNk0RERNT6e2q0HG0uW5euPJ+KHsZMMbF2hYXvvz5I6rBAevetvfUmb7kP3pxH1aLnsVSUo5x7yRlds6H5lB4P6oeLoGdvqhJTsJSWkrvaiqpK+iYrXfZvcCZ05XuzI4iJiWnX67XLfpaUktdff53Y2FimT59eczw9PZ2ffvLu+f/000+MGDGi5vjy5cuRUlJYWIjRaCQ0NJS0tDQ2btyIxWLBYrGwceNG0tLSCA0NJTAwkMLCQqSULF++nPT09Eav0aTN+/cg//k2pAxFnHMRAEeKXWzNsxMVq6d/qlbVoqGh0blpSjJHGAwot/0FMWIc8rN3Ub/6qFV2An6NXLscDh9AufAqhKJQfsTNgb0uEgf4YzJrfQE1uh7tErkqKChg+fLl9OrVi3vvvReAq666iosuuogXXniBpUuXEhERwezZswEYOnQoubm5zJw5Ez8/P26//XYAzGYzl156KQ888AAAl112WU1bhZtvvplXX32V6upq0tLSGDrUK6Tc0DWaQn3zWTCaUG68C6EoWKo8rP/ZhjlYYdgok1YZqKGh4RMYDIIRY03k59nZXejEZvEwLMOE3uBdw4ReDzfPBj8/byVftQMuvb7V1jjp8SD/8zHEJcDQDKQq2bLBTkCgoO9A7SFVo2siZFs8pnQB9k0fgTLrMURyGq5qSfaSKpwOyfhzzBi1J63TQgtvtx7aXLYuZ9t87tnhZMsGO0HBCiPHmwk0nty8kKqK/PhN5I/fIiad7207o5ze5kZ986muWoJc/CLKHQ8i0kaxd6eTTevsDBttJLaXJnPTEGfbvdnWdMltQV9E/OYyRHIaUpXkrrZirVJJH2PUHCsNDQ2fpXeSPyPHmbDZVFb8UFsyRygK4qrbENMuRv74LfL9V5Cqp5F3axrpdiOwgMqHAAAgAElEQVT/8wnE94UhI6muVtm+2UFYdx0xcZqahUbXRXOuGkBceBUA2zc7KDnkJnVYIBGR2mKgoaHh20T28ErmKPVI5gghEJddj7jw98iVWci3X0C662oWNhe5+kc4UuzNtRKCwi0OqqslqUO1pssaXRvNuWoAodezf081RdudxCf61amy0dDQ0PBVgkJ0jJtqJqSbjvWrbOzId9QksgshUH77B8Slf0SuXY76xjyky3Xa15BuN/Kbf0LvJBicTuVRD3uKqonv40dIqKYfqNG10ZyrBqgoc7Mxx0Z4pJ7UYYEdbY6GhoZGq1IjmdPLwPbNDvLW1pbMUc67FHHVrZC3GvXVuUjn6bWdkT8vhdLDKL/17gJs2WBHbxAMGKQlsWt0fTTnqgFysq34ByoMzzSiKFr4WkNDo+uh0wmGZhyXzNnjYvVPtSVzlMnTEdf9GbZuQH35caTD3qz3lW6XN2qV0A9Sh3Nov4uyEjcDUgPw89e+djS6Ptpd3gBut2TkWBP+2kKgoaHRhaklmVPmITurtmSOMm4a4qbZsGMr6oI5SJulyfeUK5dAWQnKb/+AxwP5eXaCuynEJ2rVgRpnB5rn0ABDRxkJ7qZVBmpoaJwdxPbyI3OSGbdLkp1VxZHik3lWyqgJKLf9BfYUoc5/GFnVsEardLmQ3/4TEgdAylB2bndgt0lShxoR2i5As5BS4mpBnptG50FzrhqgR0/tCUtDQ+PsIjRCz7hzzAQGKqxZbmXvzpN5VmLYaJQ7HoRD+1Cf+yvyWEW97yFX/gDlpSi/vQq7TaVou5OYXgbCI7Uk9qZwu93k5+fz0Ucf8eSTT/LZZ5+Rm5vL0aP/396Zh0dRpfv/03snnX2HLGRBdhEQBGHGZQa9js4oVwfletGfOIoKjOOGgswM8eoI6ABXnaCjMnjVeRzQO6PgdscNHEQBEVDCnoUlC9nT2Xqt8/uj00V30gkJdJJOOJ/nydPVleqqU2+dOvWt933POXV9XTRJN5GDiHZAaWlpXxdhwCAHwwse0pbBRdozME6nYNe2JirLXWQPMzHqIrPqdRIH9qLk/QGi49A+/CSauET1d/FRkVTcOxPik9A+uoxd25qpKHNy5bVRfgOWSvxpaGjghx9+YN++fdhsNuLi4hgxYgSHDx9W62dcXBw5OTlkZ2eTlJQkh7LoJr09iKgUVx0gxVXwkA+w4CFtGVykPTtGUQT5u1soPuogebDeb8occfQAyvNPQHgE2oefQpOYAkD4js00vLIK7UNPUhU3im82NzH8QjPDRskegm0RQlBWVsaePXsoKCgAICsri4suuoi0tDQSExOpqqrCarVSWFhIYWEhJSUlCCGwWCxkZ2eTk5NDamoqOp1MYTkTUlyFCFJcBQ/5AAse0pbBRdrzzBS1TpkTFa3lkh+fnjJHHDuKsnopGIxoH3oSEpLgt/eiJCTDQ3/gy382orjhip9FotNJL4sXl8vF4cOH2bNnD1VVVZhMJkaPHs3YsWOJiopStwtUN1taWiguLqawsJBjx47hcrkwGo1kZmaSnZ3NkCFDMJnkmIyBkOIqRJDiKnjIB1jwkLYMLtKeXaOizMmubU3o9Bou+ZGFmHhP/pQ4WYyy6ncAaCZOQ3zxIdpH/kCRZhj5u1uY9CMLKalyZgtoH/qLj4/noosuYvjw4RgM7W10prrpcrk4fvy46tWy2WxotVrS09PJzs4mOzsbi8XSk6fUr5DiKkSQ4ip4yAdY8JC2DC7Snl3HWudmx9Ym7DaF8ZPDGZzu6fQjyk+irPwd1FVjGDOBlnt+z+cfWomN1zP5Mst5nRskhKC0tJS9e/cGDP11Zpvu1E1FUSgvL6egoIDCwkLq6+sBSE5OVvO04uLizv2E+jFSXIUIUlwFD/kACx7SlsFF2rN72G0KO7c2UVvtZsSFZoaONKHRaBCV5Sgb/kLc7ffy5UEzJ4ocXH5NJJFR52cuUFdDf51xtnVTCEFNTQ2FhYUUFBRQUVEBQGxsrOrRSklJOe9ErxRXIYIUV8FDPsCCh7RlcJH27D5ut2DvjmZKjjtJyzRw0cRwtN6cKiWCTW+fJHu4idHjzr9pw7ob+uuMYNXNhoYGioqKKCgooKSkBEVRCA8PV4VWWloaev3AHyajt8XVwLeoRCKRSIKGd8ocS6Sdw/k2mpsamTjNgtGoYfu/qjCZPSO+ny+cS+ivN4iMjGTs2LGMHTsWu92uJsQfOnSIffv2YTAYGDJkCDk5OWRmZsqE+CAhxZWkR7DZbBQXF1NeXk5GRgbR0dHExcX1eUMjkUjOHY1Gw/AxZiIitezZ0czWTxtJG2KgstzOuEvCMBgG/n0eKPQ3fvz4boX+ehuTycTw4cMZPnw4LpeLkydPqgnxR48eRavVkpqaqnq1IiMj+7rI/RYZFuwAGRbsHkIIamtrKSoqoqioiLKyMoQQ6HQ63G7PPGVms5nU1FQGDx5MamoqCQkJaLVyYMHuIMNYwUXa89ypqXKxc2sTDrsgIcnElCvMA/olKpihv87ozbophKC8vFwVWrW1ntH3k5KS1PG0+vvL8YDMuVqzZg3fffcd0dHRrFy5EoDGxkZWr15NZWUliYmJPPjgg0RERCCEYN26dezevRuTycS8efPIzs4GYPPmzfz9738H4MYbb+SKK64AoLCwkLy8PBwOB+PHj2fOnDloNJoOj9EVpLg6M263m5KSElVQWa2e+cYSEhLIzs4mMzOT5ORkdDod+/bto6SkhJKSEnU7o9HI4MGDVbGVlJQkB8M7A1IMBBdpz+DQ3OTm4A82Jl6agiIa+ro4QacvQn99WTe9CfGFhYWUl5cDEB0drXq0Bg0a1O9ejAekuNq/fz9ms5m8vDxVXL355ptEREQwY8YM3n33XRobG5k9ezbfffcdH3/8MYsXL+bIkSO89tprPP300zQ2NrJo0SKWL18OoC5HRESwePFi5syZwwUXXMCyZcv42c9+xvjx4zs8RleQ4iowzc3NFBcXU1RUxPHjx3E6neh0OtLT08nKyiIzM7OdK7ltI9HQ0EBpaakqtrxvSXq9npSUFFJTU0lNTSUlJeW8SLTsDlIMBBdpz+Ay0OwZjF5/Z0uo2LKpqUlNiD9x4gSKohAWFkZWVhbZ2dlkZGT0i3Z6QCa0jxo1Su0O6mXnzp3k5uYCcPnll5Obm8vs2bP59ttvueyyy9BoNAwbNoympiZqa2vJz89n7Nixqudp7Nix7Nmzh9GjR9PS0sKwYcMAuOyyy9i5cyfjx4/v8BiSriOEoLq6WvVOed9iLBYLw4cPJysri7S0tG65wyMjI9W4P3gEm6/Y2r59OwBarZbk5GRVbA0aNAijUU6oLZFIepZAob+f/OQnQQ/99QcsFgtjxoxhzJgx2O12jh07RlFREUePHmX//v3o9XqGDBmiRivCws6/XqKB6DO5WV9fT2xsLOAZf8MbKqqpqSEhIUHdLj4+npqaGmpqaoiPj1fXx8XFBVzv3b6zYwTi008/5dNPPwVg+fLlfmU433A6nRQVFXHo0CEOHz6sDkiXmpqqNjDdGSdFr9ef0Z4ZGRnqcktLC8ePH+fYsWMUFxfz3Xff8e2336LVaklJSSEzM5PMzEwyMjIIDw8/+xPth3TFlpKuI+0ZXPqzPYUQHDt2jO3bt3PgwAGEEIwYMYLJkyeTlZXV6/lGoWrL1NRUpk6disvlori4mIMHD3Lw4EEKCgrQarVkZGQwcuRIRowYoT5/z0dCzpcXKErZUaXWaDQBtz8bpk+fzvTp09XvoeCO7U0aGxvVcN+JEydwuVwYDAbS09OZOHEimZmZflMpVFdXd3nfZ+Pejo+PJz4+ngkTJuBwOCgvL6e0tJSTJ0+yfft2tm3bpm7n9WwNHjx4wE/3ECqhgoGCtGdw6Y/2DBT6GzdunF/orzvtXbDoD7aMiYlhypQpTJ48mYqKCjVP66OPPuKjjz5S829zcnJISEjo04T4ARkWDER0dDS1tbXExsZSW1urVuL4+Hi/ClVdXU1sbCxxcXHs379fXV9TU8OoUaOIj4/3q/jV1dXqMP8dHUPiEbGVlZUUFhZSXFyshm0jIyMZNWoUWVlZpKamhkQs3Wg0kpGRoXq3XC4Xp06dUkOJBw4c4Pvvvwc8N7tXaKWlpcmuxBKJJCAy9Bc8NBoNycnJJCcnc+mll1JXV6cKrR07drBjxw4iIyPVhPjU1NR+lxDfXfrsyTlx4kS2bNnCjBkz2LJlC5MmTVLXf/zxx0ybNo0jR44QHh5ObGws48aN46233qKxsRGAvXv3cuuttxIREUFYWBiHDx/mggsu4Msvv+Saa67p9BjnK06nkxMnTlBUVERxcTFNTU0ADBo0iEsvvVSdfyrUu9vq9XrVWzVp0iTcbjeVlZWq2Dp69Cj5+fmARyz6iq3o6OiQPz+JRNIzhPqAnwOFmJgYJkyYwIQJE2hubqaoqIjCwkL27dvH3r17MZvNZGZmkp2dzZAhQwakmO2V3oL//d//zf79+2loaCA6Opqbb76ZSZMmsXr1aqqqqkhISOChhx5Sh2JYu3Yte/fuxWg0Mm/ePHJycgD4/PPP+cc//gF4hmK48sorASgoKGDNmjU4HA7GjRvHnXfeiUajoaGhIeAxusJA6S3onfqgqKiIkydP4na7VU9QVlYWQ4YM6fG8pd52byuKQnV1tV+SfEtLCwDh4eF+Yqs/iElf+kOooD8h7RlcQtWefdnr72wJVVueCw6Hg+PHj1NYWEhRURF2ux2dTkdGRgbZ2dlkZWX12PNoQA7F0B/pr+JKURROnTql5k95b87o6GiysrLIyspi8ODBvTqeVF83Et4BTn3FltcDajab1bG20tLSQn5g07625UBD2jO4hJo9e2vAz54g1GwZbBRFUcdJLCgooKHBMz7a4MGD1fBhTExM0I4nxVWI0J/Eld1u9wv3tbS0oNFoGDx4sCqoYmJi+sxDE2qNhBACq9XqJ7a8PSINBoPfwKbeQVBDhVCzZX9H2jO4hII9A4X+srOzGTt2bL8K/YWCLXsLIQRVVVUUFBRQWFionndcXBw5OTlkZ2eTlJR0TtdOiqsQIdTFVX19vRru8850bjKZyMzMJCsri4yMDMzm0Jg8tT80Eo2NjX5iyzuch06nY9CgQarYSklJ6dM33v5gy/6EtGdw6Ut7ulwuDh06xN69e/tN6K8zzue6abVaKSwspKCggNLSUoQQWCwWtedhampqt196pbgKEUJNXCmKQllZmRru8z78Y2NjVe9UqE5J0B8biZaWFj+xVVVVhRBCHdjUK7YGDRrUq7PI90dbhjLSnsGlL+zZn0N/nSHrpoeWlhaKi4spLCzk2LFjuFwujEajX0J8V9pgKa5ChFAQVzabjePHj1NUVMSxY8ew2WzqrOXeqWaCGZPuKQZCI2G32ykrK1PFVkVFBYqioNFoSExMVMXW4MGDe3SE4oFgy1BACIHT6SQ5OVmdfkly7vRW/Rwoob+OaHK40YRFgc1KuCF00hL6GpfLpSbEFxYWqs/E9PR0cnJyyMrK6nCsQymuQoS+Ele1tbVquM/rDjWbzaqYysjI6FVPSTAYiILA6XRSXl6uiq3y8nLcbjfgGavNK7ZSU1ODOrDpQLTluaAoCna7HZvNpn76Lgda511WFAWtVktkZCQxMTFER0cTExOjLkdFRYVUvl1/oKfr50AK/QkhqGlxcdLq4GS9gxP1dkqsDk5YHdS2uNTtLEYtieEGEi16EsINJFgMJIbrSbQYSAg3EBeuR6/t32LybFAUhfLycgoKCigoKFBnYElJSVET4r1jXoIUVyFDb4krt9tNaWmpmoxeV1cHeBopb/5UcnJySIb7usr5IAhcLhcVFRWq2CorK8PpdAKenppeoZWamkpkZORZv1kPRFsKIXC5XAFFka8gCrTe4XB0um+DwYDZbMZsNmMymdot63Q6ysrKqKuro66uTr1m4BkYsa3w8n5GRUWFxAC7oUZP1c/+HPpzK4JTjU5OWO2crHdw0mrnRL2DEquDZqeibhem15IWbSQ92khalImMpBiOV9RR2eSkqtlJVbOLyiYnjQ7Fb/9aDcSGeYRXokVPYriBhNZPjwDTE2nS9XtvXmcIIaipqVET4r2DYsfGxqpC6+KLL+7VMklx1QE9Ka68MeTi4mKOHTuGw+FAp9ORlpam5k/1t5HFHW6FqiYXFa0NQWWTk8omF7UtLqIsZgzCRaRJh8WoI8KoJcKo8/nzfA83atEOkAZAURQqKytVsVVaWordbgcgIiLCT2x1pydnKIsrRVFwOBwdeoo68yh5vX6B0Gq1qhhq+9nRsslkwmQyndHz5GtPIQQtLS3U19dTV1enfnr/2gq5joRXdHT0eSu8glk/Owr9XXTRRaSmpoacWLC7FEqsDk5aPV4oj0fKTmmDE5dy+jEbG6YnPcpIapSR9GgTadFG0qKMxIXp/c6pI1u2OBW1jfUKLs93l/rpezwAo06jCq1Ei0EVYAk+Asyk778v8G1paGhQQ4feDl/Lly/v1TJIcdUBwRRXXlXtDfeVl5cjhCA8PNwv3Beqb2BCCKx2t0c4NbmoVMWT50aubHZSb/N/OGqAuDA9sWF6XGipb7bT6FBwKh1XNw0eF7iv6LK0LnuEmdZPjKl/Ji1hem3INba+CCGorq72E1vNzc0AhIWF+Ymt+Pj4Ds+lN8RVWy9SVz1JXvHYEQaDoUuiqO06g8HQY9e2q/YUQmCz2fwEl68Is9lsfttHRER0KLxC9T4PBsGon6Ee+rPa3Zz0EU8eMeWgssmJt3XTaiA5wkBalIn06NNCKjXKSISxa6Hms7WlIgRWm5vK5tPtdVWTk0pViLn8wo5eoky6DkOPiRY9MWY9un4YfrTb7RQXF6uDjvcWUlx1wLmKK5fLpQ6QVlxcrMaDk5KS1HDfuY7bESwcboXq1huv0ucm9IqnqmYnDrd/NTG1vgklWgwkWfzd0IkWPXFhBgw6z7n5NhJ2l0Kjw02jw/vpptF++nuTw02DQ6HJ+z+H0vp/N+5OaqpWg58gi2wVXl5B5ivOIr3rTZ5tTDpNr18HIQR1dXWq2PId2NRkMvnlbCUmJqph4e6Iga56kdoud+ZF0mg0XfYetRVNoZi/FCyx6iu82gow7+wAXiwWS7v8Lu+n0Wg857L0Jediz1AK/QkhqGp2+XigPOG8k/UO6u2n7w+jTkNqlMfzlBZt8nxGGRkcZcSoOzdPUE++SDlb23yv58srxHyjDi0u//CjTgPx4f7eLu8zICFcT4LFgMUQui+5MucqRDgbceWdQ6m4uJjjx4/jdDrR6/Wkp6erHqquTr8TLIQQNNjdbQSTz1tMk5NaW/uHaWyYXn1z8Qqm0+LJQISx6zdRMBoJIQQ2lzgtyNoIr8ZWQdbgs+wVbk1OhU4cZui1GtUb5hFmrcsmj1iLNLYJZ5pOe9bOtQH1xWq1ql6tkpISNf/OYDAwaNAgUlNTGTJkCNXV1WcUTXa7nc5ubb1e36HXqDOhZDQae6XxFELgVAQOl8ChCJxuBbtb4HQLHC6ldZ3A7lY869wCh1tp/Wzdzu/76WXveqdbYDEbidAL4sL0xITpiTXriQ3TEdvqdY0xn3uysN1ubye8vJ9e76WX8PDwDj1e/aEjS3fv9b4O/bkUQVlDa0K51U5JvSehvMRqx+Y6ff9EGLWkRZn8cqLSoowkWgw95s3p6xSAJoe7TejRPwRZ3exs98Ibpte2y/dKsPgk44frMQSxzewOUlyFCF0RV95RZb3hvlOnTgGekIA3dyotLa1H8y+cbkF1s7M1VBfY+9TW62T08Toltnn7SLIYiA/yDdDXjYQiBC3OQB6z08tNDqVVmLlbhZmiCrPOMOo0fqFKi1FHpOl0ODNQCNO7fKaHdlNTk5/Yqq6ubrdNd/OQvJ9drZMuxSNGTguYNkKnjWgJLHR8t+lA6Cj+oqltne0uOg0YdFqMOo365/9di0GnQdHoqbA2U2tz02AP7LGLMulU0XVagHn/dOr38LN4a3c4HB2GGr0Tq3sJCwsLKLxiYmJCRnh19V7v7dBfi1NRPU/enKgSq4OyBoefQEgI1/t5obw5UdF9kBDe1+3mmXArgjqbq53o8gqxqiann5fPS6xZR4JPuLFtLliUWdcjubdSXIUIHYkrl8ulTjVTVFSkNoApKSmqdyohISEoN6IQgkaH4iOY2guouhYXbS9gjFnXTjwltIbvEvug50ioNxKd4VYEzU6lA29ZG7Hm9ZjZPaFNm6tzYWbWa2ibXxZp8g9n+go0g+Ik3KilodkGeiNCa8CliHZCx6kI7K5WoeMjWhwugVPpgtDxWe7M49cVAgsbDQatFqNeg1GrwajXYtRqMOj8l02tAsh3HyZd63YBhJLRZ31XvQm+ddPp9jwsaltc1LZ+1rW41WX1z+ZulzDsPVevt8tXdMX6CTJdl3NXnE5nh6FGbwjZi9ls7jDU2JPjrrXlTPd6T4b+hBDU29ztEspPWB1UN5/OMdJpYFCksTWRvDWU15oXFUpjSvXndtOL3dWacuJNwG+TA1bV5MTe5kVKr9WoYitQ6DEhXH9W10mKqxDBV1w1NjaqYurkyZO4XC4MBgMZGRmqoDqbmbxdSqvXqck/7l3R5FTfBHxd0+BpwH0Vv7fXR1JrBYwP1wc1VBUMBkIjcTa4FHHaC6bmlrUXZGr4snW5weE+Z8+NLzoNquhoL0zae3TabhdI6HS8v9PbGbS9n8vWXc6mbnpfenxFl0eUuX0EmOezbbd58HTciDJ5Qo8eT5gugAjzCLGOOmq4XK4OhZd3AlwvJpOpw1BjWFhYUK9RIHsGO/SnCEFFo9MjnlqHNfDmRPna26zXkBplIj3KR0hFG0mJMKr5oKHM+dBuCiFocCitYqtVfPl6wZqd1La42r3knc3YX1JchQi7d+9WBVVlZSUAUVFRarhv8ODBnYZWhBA0ORSfnnUuP+9TVZOTmgBep2iTTs1xSmgVT2rCuMXQJ+7pc+V8aCSCjdOttBFlnmVDWDiOlmYMWo3H86PTtnp/PGLGqPP3CBm0XffinI/0dN10uhWP6LJ5vMw1vkKsjTgL5Og0+XnDfPLB/ESYnmiTTr3OLpcLq9UaMNTY0NDgl4tnNBoDerxiYmLOSnj52jNQ6G/MmDFceOGFXQr9Od2nhzZQE8qtnvGhfF8+ok06VTylR58O6cWH6/v10C6y3fTgVjyDrfrmfamhx9bna1fG/nr85+N6tdxSXHXAokWL0Gg0DBo0SPVOxcXFqY2NSxHUNLcflqCz3hZ6rcZnkDfPRU9SY88Db6wRL7KRCB7SlsElVOyptHrDfAVYTYtHkPkJMZuLpg68YdGtHjBViPl6xFoFWZRRg7OlMaDHy2q1+gkvg8HQYajRYrEEFF4JCQkUFRV1K/TX5PAJ5bXmRJ202jnV6FQ9FhogKcKg9sZLi/Z4pFKjTUSZQieUF0xCpW72B7oy9tfXD8uhGEKCj/75KWEJg7G69afDdK1uyoqmwK5K33FCAg1REN1DiXqhjmwkgoe0ZXDpj/Z0uJVWb5dbFWCnPWIuP09ZoOiyWa8hxqw/3UOyVYjFmLSEKTb0jkaErRF7UwNWq0eAWa1WFOW0qNPr9QFDjMXFxezfvx/wD/0B7aZ6OdnqlfIdc0mv1ZAaeToHKr3VC5UaZRyQL56d0R/rZqiiCEFaaz3sLc7PYYS7wO/3aoFy9bteixrbHZsc7jNEgSfWm2AxYD7Pbn6JRNL7GHVakiOMJJ9hVBdFCBrtrQLMV4jZvB4xF8fq7OwtawrQKzYcrSacaNNgYhL0xKVpidE4iBAtmNxN6BxNKC0NVFRVU1RUpAovszmMC0ZfRHjaBVQ6jfzvCTsn9h1rN9VLuEFLWpSR8YMsrR4oI+lRJpIjem5oA8n5S184NaS46oA7xif69VKIDevf8XuJRHJ+odVoiDLriTKfuZm3uxSfUKS7fWjS5qaoRUOdzYgijECs54d6IEYQrbUTq3VSJizYy7RQVg+cnurliqwoNaE80FQvEslA47wQV3v27GHdunUoisJPf/pTZsyYccbf/Puo+F4omUQikfQ9Jr3XG9b5CPFK61RYp0ORpz1iVrubafGRxBvcpHVzqheJZKAx4MWVoiisXbuW3/72t8THx7N48WImTpxIWlpaXxdNIpFI+hVajSdfK8asJzO2/f9lnpBE4mHAJwkdPXqUlJQUkpOT0ev1TJ06lZ07d/Z1sSQSiUQikQxQBrznqqamhvj40yG++Ph4jhw50m67Tz/9lE8//RSA5cuXk5CQ0GtlHOjo9XppzyAhbRlcpD2Di7Rn8JC27N8MeHEVaKSJQImU06dPZ/r06ep36doOHjJUEDykLYOLtGdwkfYMHtKWwaW3R2gf8GHB+Ph4vwlvq6uriY0NkCwgkUgkEolEEgQGvLjKycmhrKyMiooKXC4X27ZtY+LEiX1dLIlEIpFIJAOUAR8W1Ol03HnnnfzhD39AURSuvPJK0tPT+7pYEolEIpFIBigDXlwBTJgwgQkTJvR1MSQSiUQikZwHDPiwoEQikUgkEklvIidulkgkEolEIgki0nMVgEWLFvV1EQYU0p7BQ9oyuEh7Bhdpz+AhbRlcetueUlxJJBKJRCKRBBEpriQSiUQikUiCiC43Nze3rwsRimRnZ/d1EQYU0p7BQ9oyuEh7Bhdpz+AhbRlcetOeMqFdIpFIJBKJJIjIsKBEIpFIJBJJEJHiSiKRSCQSiSSI9CtxtWPHDm6++WZKSkrUdRUVFTz88MN9WCrIz89n+fLlfVqGnua2225Tl7/77jvuv//+bs/Y3tm1Wr9+Pd9///05lTHU6a4NN2zYwMaNGwHIy8vjm2++6fEyhgrBqG++9mvLb3/7W+Dc79358+djtVrP+vd9wS233MLChQt5+OGHWbVqFXa7vdPtu3KOvtfLy7naNjc3l4KCgjNu19DQwMKFCxSewmsAABVDSURBVFm4cCF3330399xzDwsXLuSOO+7gwQcfDPib/tbe5Ofnc+jQIfX7QG4PzuZ6tuWf//wnW7Zs6eGSdk6/mv5m69atjBgxgq+++oqbb765r4tzXvLDDz+wbt06lixZQkJCQtD2e8sttwRtX6FOT9lwINJTtnrqqaeCtq/+htFo5NlnnwXg+eef55NPPuHnP/95H5fq7ImMjFTPZ8OGDZjNZq6//noqKipYsWJFwN901N4oioJWG3o+h/z8fMxmM8OHD++T47vdbnQ6Xa8c62yupy9ut5urr766x8rX1TrSb8SVzWbj0KFDLF26lGeeeSaguKqoqOBPf/qT+iZ25513Mnz4cPLz89mwYQMRERGUlpYycuRI7rrrLrRaLbfddhtXXXUV+fn5WCwWHnjgAaKiosjNzeW2224jJycHq9XK4sWLycvL46WXXlLfpmpqarjmmmsYNWoUNpuNlStXcuLECbKzs/n1r3+NRqPhnXfeYdeuXTgcDoYNG8bcuXPRaDS9artgceDAAf785z+zePFiUlJSAKisrOTFF1/EarUSFRXFvHnzSEhIoK6ujldeeYWKigoA7rrrLmJjY1EUhZdeeonDhw8TFxfHo48+itFoJC8vj4svvpgpU6b05Sn2ON2xYUd0VKdyc3PJzMykqKgIq9XK/Pnzeffddzl+/DhTp05l1qxZvXWaQaE7tlq4cKH6u9LSUpYsWQLAyZMnyc3NpaqqimuvvZZrr70W8Hha3njjDb/jHT16lJdffpmHH34Yi8XCmjVrqKiowGQyMXfuXIYMGUJDQwPPPfccVquVoUOH4tsf6Msvv+Sjjz7C5XJxwQUXqG1MKDNixAiOHz8OwDPPPEN1dTVOp5Nrr72W6dOnt9v+TNtYrVZWrFjBTTfdhMlkUtf72rahoYHXXnsNh8OB0Whk3rx5DB48GIfDwZo1azh58iSpqak4HI5zPr+utDfz58/nyiuvZO/evVxzzTV88sknAdv+xx9/nPvuu4/09HTA41m7/fbbURQl4Pls3ryZb7/9FrvdzqlTp7jkkkuYPXs2AK+88goFBQU4HA6mTJmiPs/mz5/P5Zdfzq5du3C5XDz00EMYDAY++eQTtFot//rXv7jzzjsB2L9/P++//z51dXXMnj2bKVOmYLPZeOaZZ2hqasLlcjFr1iwmTZpERUUFTz/9NEOHDqW4uJhBgwaxYMECTCYT8+fPZ9myZURFRVFQUMAbb7xBbm4uGzZsoLa2lsrKSiIjI/mP//iPTp+v0dHRHDt2jEsuuYSMjAw+/PBDHA4HCxcuVO/fnrqeubm5DBs2jEOHDjFx4kRaWlpUUfbhhx/yySefoNPpSEtL44EHHmD//v2sW7cOAI1GwxNPPEFhYSGbNm1SBxtdu3YtOTk5XHHFFe3qyLRp085cWNFP2LJli1izZo0QQoglS5aIgoICIYQQp06dEg899JAQQgibzSbsdrsQQojS0lLx2GOPCSGE2Ldvn7j11ltFeXm5cLvd4r/+67/E119/LYQQYubMmeLLL78UQgjx9ttvi1dffVUIIcTSpUvF0aNHhRBC1NfXi3nz5vmVp6KiQvzmN78RFRUVYt++feL2228XVVVVwu12i8cff1wcOHBACCFEQ0OD+pvnn39e7Ny5M/jG6QVmzZol7rjjDlFcXOy3ftmyZeKLL74QQgjx2WefiRUrVgghhFi1apV4//33hRBCuN1u0dTUJE6dOiVuueUWUVRUJIQQYuXKlWLLli1CCCH+9Kc/qddkoNJdG65fv1689957Qgh/+3RUp5YuXSreeOMNIYQQH3zwgZg7d66oqakRDodD3HPPPcJqtfbo+QWT7trKy86dO8Xvfvc74XQ6xfr168WSJUuEw+EQ9fX1Ys6cOcLpdAohhJg9e7YQwtM2LFu2TBw8eFA8+uijorKyUgghxNq1a8WGDRuEEEL88MMP4pFHHlHXv/3220IIIXbt2iVmzpwp6uvrxYkTJ8SyZcvU/b/yyiti8+bNPWCZc8d77i6XS6xYsUL83//9nxDidL2y2+3ioYceUuvLvHnzRH19fafbzJ49W9TW1orFixeLvXv3CiE6tm1TU5NwuVxCCCH27t0rnn32WSGEEJs2bRJ5eXlCCCGKi4vFLbfcorbBXcX3nulqezNv3jzx7rvvqvvoqO3ftGmTWL9+vRBCiJqaGnH//fd3ej5ffPGFmD9/vmhqahJ2u13cd999qg28dnS73WLp0qVqPZ83b5748MMPhRBCfPzxx+LFF19sd17e8q9cuVK43W5x4sQJsWDBAiGE55o2NTWpZV+wYIFQFEWcOnVKzJw5U30u5eXlqfvzvb5Hjx4VS5cuVY/56KOPqs/Uzp6v/+///T+1rZk7d65qpw8++ECsW7euaxcvAF29nkuXLhWvvPJKwN/NnTtXOBwOIYQQjY2NQghPO+K1RUtLi3C5XGp99fLqq6+qbU3bOtIV+o3n6quvvuK6664DYOrUqXz11Vftxqxwu92sXbuW4uJitFotZWVl6v+GDh1KcnIyANOmTePgwYNMmTIFjUbD1KlTAfjxj3/MH//4xzOWxeFwsGrVKu68804SExOpqKhg6NChxMfHA5CZmUlFRQUjRoxg3759bNy4EbvdTmNjI+np6UycODEoNulNdDodw4cP5/PPP2fOnDnq+iNHjvDII48AcNlll/HXv/4VgH379rFgwQIAtFot4eHhNDY2kpSURGZmJuAZc6SysrJ3T6QP6a4NO6KzOuX9zMjIIC0tjdjYWACSk5Oprq4mMjKyJ04t6JyNrcrKynjzzTf5/e9/j17vadomTJiAwWDAYDAQHR1NfX29ep96KSkp4eWXX2bJkiXExcUBcPDgQTU/cMyYMTQ2NtLc3MyBAwfU40+YMAGLxQJ4rklRURGLFy8GPG1EVFRUT5jmnPF6EwBGjhzJT37yEwA+/PBDdu7cCUBVVRVlZWXt6ktH27jdbp588kl+9atfMWrUKHX7QLZtbm4mLy+P8vJywNNug8cT4/UsDhkyhCFDhpzzuXa1vfE+Azpj6tSpPPnkk9x88818/fXXqpe9o/MBT90JDw8HIC0tjaqqKhISEti2bRufffYZbreb2tpaTp48qZ7v5MmT1fLu2LGjw/JMmjQJrVZLWloa9fX1AAgheOuttzhw4AAajYaamhr1f/Hx8YwYMQLw3Dsffvgh119/fafnPHHiRIxGo3peHT1fc3Jy1LYmJSWFsWPHAp52aN++fWe0bVfp7Hp2dA0zMjJ4/vnnmTRpEpdccgng8di+/vrr/OhHP2Ly5Mnt2oRAdKWO+NIvxFVDQwP79u3jxIkTaDQaFEUBUF2sXt5//32io6N59tlnEULwn//5n90+ljdkp9PpVJe/0+n02+aVV15h8uTJagUCMBgM6rJWq0VRFBwOB2vXrmXZsmUkJCSwYcOGoLi6+wKNRsODDz7Ik08+yd///nduvPHGs9pPWzv1V3ucDcGw4ZnqlNe+Go3Gz9Yajcav0Q91umsrm83G6tWrueeee9SHOKCKLPDUt0A2iImJwel0UlxcrP5WdDL8X6CwvhCCyy+/nFtvvfWM59bX+OZcecnPz+eHH37gqaeewmQykZub267d62wbnU5HVlYWe/bs8RNXgWy7fv16Ro8ezcKFC6moqOCJJ57osXPtanvjG8LsqO2Pi4sjMjKSY8eOsW3bNubOnQt0fj5tj+92u6moqGDTpk0sW7aMiIgI8vLy/I7jrbMd1ddA+/aWd+vWrVitVpYvX45er2f+/PnqObett97vWq22w2edr106e762bWt82yHv8zoYdHY9fcvqy+LFi9m/fz/ffvst//u//8uqVauYMWMGEyZM4LvvvmPJkiX87ne/87vu0LktukJoJwS08s0333D55ZezZs0a8vLyePHFF0lKSuLgwYN+2zU3NxMbG4tWq+XLL7/0u6hHjx6loqICRVH4+uuvVQUvhFB7XXgT5gESExMpLCxUj+/l448/pqWlhRkzZpyx3N6LExUVhc1mY/v27edghb7HZDKxaNEitm7dyueffw7AsGHD2LZtG+BvvwsvvJB//vOfgCdO3tzc3DeFDjG6Y8NADLQ61RndsdWaNWu44oorGDlyZLePY7FYWLRoEW+99Rb5+fmAx6Pzr3/9C/CIisjISMLDw/3W7969m6amJsBT37/55hvVS9DY2NivvLLNzc1YLBZMJhMlJSUcOXKk29vMmzeP0tJS3n33XXVdINs2NzerQmvz5s3qtqNGjWLr1q0AHD9+nGPHjgX7NLtER20/eLwX7733Hs3NzWRkZAAdn09HNDc3YzabCQ8Pp66ujj179pzxN2FhYdhsti7tOzo6Gr1ez759+/zqYFVVFYcPHwb8752kpKQOz7ftvjt6voYqiqJQVVXFmDFjmD17Ns3NzdhsNsrLy8nIyGDGjBlkZ2dTUlJCQkICJ0+exOl00tzczA8//HBOx+4XnquvvvqqnZiZPHkyW7du5YYbblDX/du//RsrV67km2++YfTo0X5Kc9iwYfz1r3/l+PHjjBw5UnUPmkwmTpw4wWOPPUZ4eLja1fMXv/gFq1ev5ssvv2TMmDHqfjZt2oRer1fd6ldddRWpqakBy22xWPjpT3/Kww8/TFJSEjk5OcExSB8SERHB448/ztKlS4mMjGTOnDm8+OKLbNy4UU0wBrjjjjt4+eWX+fzzz9Fqtdx9993ExMT0celDg67aMBADsU51RldsVVlZyfbt2ykrK+OLL74A4N577+3WcWJiYnjsscd4+umnue+++7j55ptZs2YNjzzyiJr0CzBz5kyee+45HnvsMUaOHKl2PEhLS2PWrFk89dRTCCHQ6XT86le/IjExMbgG6SHGjRvHJ598wiOPPMLgwYO54IILur2NVqvlgQceYMWKFYSFhZGWlga0t+0NN9xAXl4eH3zwAaNHj1Z/f/XVV6s2z8zMZOjQoT170h3QUdsPMGXKFF577TVuuukmdV1H59MRmZmZZGZmqvdwV3oAXnzxxaxatYqdO3eqCe2B+NGPfsSKFStYtGgRmZmZfs+m1NRUNm/ezMsvv0xKSorao+6Xv/wlL730Ev/4xz86tXlnz9dQRVEUXnjhBfXl/rrrrsNisbB+/Xry8/PRarWkpqYyfvx4DAYDl156KY888giDBg0iKyvrnI59Xkx/k5+f79cLwJdAvYYkEolEIhkoeIcxWLlyZV8X5byhX4QFJRKJRCKRSPoL54XnSiKRSCQSiaS3kJ4riUQikUgkkiAixZVEIpFIJBJJEJHiSiKRSCQSiSSISHElkUj6lJdffpl33nknKPuqqqritttu6xdj8EgkkoGLTGiXSCQ9xvz586mrq0On06lTdVx22WVMnz6925Maz58/n3vuucdvZoSzJT8/nxdeeIGXXnqpw23y8vLYsmWLOuEtQHl5Offffz8bNmw45zJIJJKBi/RcSSSSHuWxxx7j9ddfZ82aNcyYMYP33nuvU1FztvTE9D4RERH87W9/C/p+JRLJwKZfjNAukUj6P+Hh4UycOJGYmBiWLFnCz3/+czIyMsjLyyM+Pp5Zs2ZhtVpZs2YNBw8eRKPRkJ6eTm5uLnl5eVRVVbFixQq0Wi2//OUvufTSS1mwYAH33nsvb7/9NklJScyfP58FCxbw1ltvodPpaGxs5PXXX2fv3r04HA5GjhzJ/fffz9NPP43L5eK2224D4LnnnvObk9DL5ZdfztatW9m/f7/fnHlevvjiCzZu3Eh1dTVRUVHccMMNXHXVVcBp79jPfvYzNm3ahFar5a677kKv1/M///M/WK1WfvGLX6jzJiqKwsaNG/nss89oampizJgxzJ07l4iIiB68KhKJpCeQ4koikfQqQ4cOJS4ujoMHD6rzs3l5//33iYuL49VXXwXgyJEjaDQafv3rX3Pw4EG/sGBFRQUA+/fvZ/Xq1Wi1Wurq6vz298ILL2A2m1m5ciVms5nDhw9jNpt5/PHHzxgWBM/0WP/+7//OW2+9xZNPPtnu/9HR0Tz22GMkJydz4MABnn76aXJycsjOzgagrq4Op9PJSy+9xObNm/nzn//M2LFjWb58OVVVVSxatIhp06aRnJzMRx99xM6dO8nNzSUqKop169bx6quv8sADD5ydoSUSSZ8hw4ISiaTXiYuLo7Gxsd16nU5HXV0dVVVV6PV6Ro4ciUaj6XRfM2fOxGw2YzQa/dbX1tayZ88e7r77biIiItDr9QG9T2fiqquuoqqqit27d7f734QJE0hJSUGj0TBq1CjGjh3rN6G8TqfjxhtvRK/XM23aNBoaGrj22msJCwsjPT2dtLQ0dYLiTz/9lFmzZhEfH4/BYGDmzJls3769R8KdEomkZ5GeK4lE0uvU1NQEDHddf/31vP322zz11FMATJ8+vd2k7W2Jj48PuL66upqIiIhzDqsZDAZuuukm1q9fz29+8xu//+3evZt33nmH0tJShBDY7XY/b1xkZKSauO8Vf9HR0er/jUYjNpsNgMrKSv74xz/6iUmtVkt9fX3AkKVEIgldpLiSSCS9ytGjR6mpqWHEiBHt/hcWFsbtt9/O7bffzokTJ3jiiSfIycnhwgsv7HB/HXm24uPjaWxspKmpCYvF0qXfdMSVV17Jxo0b2bFjh7rO6XSycuVKFixYwMSJE9Hr9TzzzDPd2m/b8t53330B7SKRSPoXMiwokUh6hebmZnbt2sVzzz3Hj3/843b5VgC7du2ivLwcIQRhYWFotVrV8xMTE6PmWXWF2NhYxo0bx6uvvkpjYyMul4v9+/cDHu9RQ0MDzc3NXdqXTqdj5syZvPfee+o6l8uF0+kkKioKnU7H7t27+f7777tcvrZcddVV/O1vf6OyshIAq9XKzp07z3p/Eomk75CeK4lE0qOsWLECnU6HRqMhLS2N6667jquvvjrgtmVlZfzlL3/BarVisVi4+uqrGT16NAAzZszgL3/5C2+++SY33ngjU6ZMOeOxf/3rX/Paa6/x4IMP4nK5GD16NKNGjSI1NZVp06axYMECFEVh1apVZwy9TZs2jXfffVfNFQsLC2POnDmsXr0ap9PJxRdfzMSJE7tpndNce+21ADz11FPU1tYSHR3NpZdeyqRJk856nxKJpG+Qg4hKJBKJRCKRBBEZFpRIJBKJRCIJIlJcSSQSiUQikQQRKa4kEolEIpFIgogUVxKJRCKRSCRBRIoriUQikUgkkiAixZVEIpFIJBJJEJHiSiKRSCQSiSSISHElkUgkEolEEkT+P1gCMeF+MbfCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kl_to.plot(kind='line',figsize=(9,4))\n",
    "\n",
    "plt.title('State-wise Population')\n",
    "plt.ylabel('Population')\n",
    "plt.xlabel('District Name')\n",
    "\n",
    "plt.show() # need this line to show the updates made to the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kl_to.plot(kind='kde',figsize=(9,4))\n",
    "\n",
    "plt.title('State-wise Population')\n",
    "plt.ylabel('Population')\n",
    "plt.xlabel('District Name')\n",
    "\n",
    "plt.show() # need this line to show the updates made to the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}